{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "WZP_dkz6IUQ6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-08 22:49:54.624855: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n"
     ]
    }
   ],
   "source": [
    "# 导入相关包\n",
    "import os\n",
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# 导入预训练CNN\n",
    "from tensorflow.keras.applications import efficientnet\n",
    "from tensorflow.keras.layers.experimental.preprocessing import TextVectorization\n",
    "\n",
    "import json\n",
    "import jieba # ! pip install jieba\n",
    "import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.5.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 打印版本\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YQPH6-olIUQ8",
    "outputId": "92c12512-f5b2-40bd-bcc7-dbd82354a531"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "5rVFuK7zIUQ8"
   },
   "outputs": [],
   "source": [
    "\n",
    "# 图片地址\n",
    "IMAGES_PATH = \"./ai_challenger_caption_validation_20170910/caption_validation_images_20170910/\"\n",
    "\n",
    "# 目标大小\n",
    "IMAGE_SIZE = (299, 299)\n",
    "\n",
    "# 词汇量大小\n",
    "VOCAB_SIZE = 10000\n",
    "\n",
    "# 输出句子单词长度\n",
    "SEQ_LENGTH = 25\n",
    "\n",
    "# 特征向量长度\n",
    "EMBED_DIM = 512\n",
    "\n",
    "# 输出层维度大小\n",
    "FF_DIM = 512\n",
    "\n",
    "# 参数\n",
    "BATCH_SIZE = 64\n",
    "EPOCHS = 30\n",
    "AUTOTUNE = tf.data.AUTOTUNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 准备数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "stra = '一个双手握拳的运动员跪在平坦的运动场上'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache /tmp/jieba.cache\n",
      "Loading model cost 0.809 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    }
   ],
   "source": [
    "token  = [i for i in jieba.cut(stra)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'一个 双手 握拳 的 运动员 跪 在 平坦 的 运动场 上'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\" \".join(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'一个' in token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "giQgsn7QIUQ-"
   },
   "outputs": [],
   "source": [
    "token_len = []\n",
    "\n",
    "def load_captions_json(filename):\n",
    "    \n",
    "    caption_mapping = {}\n",
    "    text_data = []\n",
    "    images_to_skip = set()\n",
    "\n",
    "    with open(filename) as f:\n",
    "        # 读取json文件\n",
    "        json_data = json.load(f)\n",
    "        # 遍历\n",
    "        for item in tqdm.tqdm(json_data):\n",
    "            # 文件名\n",
    "            img_name = item['image_id']\n",
    "            img_name = os.path.join(IMAGES_PATH, img_name.strip())\n",
    "            # 遍历5个标注\n",
    "            for caption in item['caption']:\n",
    "            \n",
    "                # 分词\n",
    "                tokens =[word for word in jieba.cut(caption)]\n",
    "             \n",
    "                # 根据tokens构造caption（打空格）\n",
    "                caption = \" \".join(tokens)   \n",
    "                \n",
    "                # 统计一下长度\n",
    "                token_len.append(len(tokens))\n",
    "                \n",
    "                if len(tokens) < 3 or len(tokens) > SEQ_LENGTH:\n",
    "                    images_to_skip.add(img_name)\n",
    "                    continue\n",
    "\n",
    "                # 如果文件名以jpg结尾，且标注不在images_to_skip中\n",
    "                if img_name.endswith(\"jpg\") and img_name not in images_to_skip:\n",
    "                    # 增加开始和结束token\n",
    "                    caption = \"<start> \" + caption.strip() + \" <end>\"\n",
    "                    text_data.append(caption)\n",
    "\n",
    "                    if img_name in caption_mapping:\n",
    "                        # 追加\n",
    "                        caption_mapping[img_name].append(caption)\n",
    "                    else:\n",
    "                        # 初始化\n",
    "                        caption_mapping[img_name] = [caption]\n",
    "                        \n",
    "        # 如果文件名在images_to_skip中，则将caption_mapping中的元素删除掉\n",
    "        for img_name in images_to_skip:\n",
    "            if img_name in caption_mapping:\n",
    "                del caption_mapping[img_name]\n",
    "                \n",
    "        return caption_mapping, text_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30000/30000 [00:20<00:00, 1476.20it/s]\n"
     ]
    }
   ],
   "source": [
    "# 加载数据\n",
    "captions_mapping, text_data = load_captions_json(\"./ai_challenger_caption_validation_20170910/caption_validation_annotations_20170910.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.array(token_len).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mRunning cells with 'Python 3.10.6 64-bit' requires ipykernel package.\n",
      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
      "\u001b[1;31mCommand: '/usr/local/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
     ]
    }
   ],
   "source": [
    "# captions_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mRunning cells with 'Python 3.10.6 64-bit' requires ipykernel package.\n",
      "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
      "\u001b[1;31mCommand: '/usr/local/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
     ]
    }
   ],
   "source": [
    "# text_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# captions_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def train_val_split(caption_data, train_size=0.8):\n",
    "    # 分成训练集和测试集\n",
    "\n",
    "    # 获取所有图片的文件名\n",
    "    all_images = list(caption_data.keys())\n",
    "    # 打乱顺序\n",
    "    np.random.shuffle(all_images)\n",
    "\n",
    "    # 分成训练集和测试集\n",
    "    train_size = int(len(caption_data) * train_size)\n",
    "\n",
    "    training_data = {\n",
    "        img_name: caption_data[img_name] for img_name in all_images[:train_size]\n",
    "    }\n",
    "    validation_data = {\n",
    "        img_name: caption_data[img_name] for img_name in all_images[train_size:]\n",
    "    }\n",
    "\n",
    "    return training_data, validation_data\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, valid_data = train_val_split(captions_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23896, 5975)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data),len(valid_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tv0Ha3jOIUQ_"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 文本向量化\n",
    "# 将原始文本中词汇变成类似one hot encoding的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "NpVpD7JoIURA"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-08 22:50:17.885237: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n",
      "2022-09-08 22:50:17.910457: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:57:00.0 name: NVIDIA RTX A5000 computeCapability: 8.6\n",
      "coreClock: 1.695GHz coreCount: 64 deviceMemorySize: 23.69GiB deviceMemoryBandwidth: 715.34GiB/s\n",
      "2022-09-08 22:50:17.910485: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "2022-09-08 22:50:17.913655: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n",
      "2022-09-08 22:50:17.913715: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n",
      "2022-09-08 22:50:17.914851: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10\n",
      "2022-09-08 22:50:17.915200: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10\n",
      "2022-09-08 22:50:17.916064: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.11\n",
      "2022-09-08 22:50:17.916774: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11\n",
      "2022-09-08 22:50:17.916958: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n",
      "2022-09-08 22:50:17.917640: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2022-09-08 22:50:17.918307: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-09-08 22:50:17.927129: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:57:00.0 name: NVIDIA RTX A5000 computeCapability: 8.6\n",
      "coreClock: 1.695GHz coreCount: 64 deviceMemorySize: 23.69GiB deviceMemoryBandwidth: 715.34GiB/s\n",
      "2022-09-08 22:50:17.928570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2022-09-08 22:50:17.928652: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "2022-09-08 22:50:18.644608: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2022-09-08 22:50:18.644633: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 \n",
      "2022-09-08 22:50:18.644640: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N \n",
      "2022-09-08 22:50:18.645922: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 22358 MB memory) -> physical GPU (device: 0, name: NVIDIA RTX A5000, pci bus id: 0000:57:00.0, compute capability: 8.6)\n"
     ]
    }
   ],
   "source": [
    "# 文本向量化\n",
    "\n",
    "def custom_standardization(input_string):\n",
    "    # 全部转为小写\n",
    "    lowercase = tf.strings.lower(input_string)\n",
    "    return tf.strings.regex_replace(lowercase, \"[%s]\" % re.escape(strip_chars), \"\")\n",
    "\n",
    "\n",
    "\n",
    "strip_chars = \"!\\\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\"\n",
    "strip_chars = strip_chars.replace(\"<\", \"\")\n",
    "strip_chars = strip_chars.replace(\">\", \"\")\n",
    "\n",
    "\n",
    "vectorization = TextVectorization(\n",
    "    max_tokens=VOCAB_SIZE,\n",
    "    output_mode=\"int\",\n",
    "    output_sequence_length=SEQ_LENGTH,\n",
    "    standardize=custom_standardization,\n",
    ")\n",
    "\n",
    "vectorization.adapt(text_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['',\n",
       " '[UNK]',\n",
       " '的',\n",
       " '<start>',\n",
       " '<end>',\n",
       " '一个',\n",
       " '在',\n",
       " '上',\n",
       " '男人',\n",
       " '着',\n",
       " '穿着',\n",
       " '女人',\n",
       " '有',\n",
       " '两个',\n",
       " '站',\n",
       " '人',\n",
       " '里',\n",
       " '拿',\n",
       " '戴着',\n",
       " '右手',\n",
       " '双手',\n",
       " '球场上',\n",
       " '走',\n",
       " '左手',\n",
       " '道路',\n",
       " '和',\n",
       " '坐在',\n",
       " '运动场',\n",
       " '球衣',\n",
       " '三个',\n",
       " '旁边',\n",
       " '舞台',\n",
       " '上衣',\n",
       " '运动服',\n",
       " '房间',\n",
       " '一群',\n",
       " '旁',\n",
       " '帽子',\n",
       " '前面',\n",
       " '包',\n",
       " '踢足球',\n",
       " '黑色',\n",
       " '衣服',\n",
       " '前',\n",
       " '室内',\n",
       " '足球',\n",
       " '草地',\n",
       " '大厅',\n",
       " '运动员',\n",
       " '一位',\n",
       " '室外',\n",
       " '白色',\n",
       " '话筒',\n",
       " '裙子',\n",
       " '东西',\n",
       " '眼镜',\n",
       " '屋子里',\n",
       " '街道',\n",
       " '放在',\n",
       " '四个',\n",
       " '坐',\n",
       " '干净',\n",
       " '面带微笑',\n",
       " '深色',\n",
       " '足球场',\n",
       " '身穿',\n",
       " '平坦',\n",
       " '打',\n",
       " '孩子',\n",
       " '人旁',\n",
       " '墨镜',\n",
       " '抱',\n",
       " '抢',\n",
       " '打篮球',\n",
       " '球服',\n",
       " '明亮',\n",
       " '争抢',\n",
       " '插',\n",
       " '头发',\n",
       " '短袖',\n",
       " '抬起',\n",
       " '表演',\n",
       " '宽敞',\n",
       " '房屋',\n",
       " '手里',\n",
       " '裤子',\n",
       " '交谈',\n",
       " '桌子',\n",
       " '小孩',\n",
       " '男士',\n",
       " '衣着',\n",
       " '椅子',\n",
       " '广告牌',\n",
       " '女孩',\n",
       " '蹲',\n",
       " '房屋里',\n",
       " '背着',\n",
       " '外套',\n",
       " '不同',\n",
       " '红色',\n",
       " '西装',\n",
       " '短裤',\n",
       " '女士',\n",
       " '给',\n",
       " '各异',\n",
       " '旁有',\n",
       " '搂',\n",
       " '说话',\n",
       " '拎',\n",
       " '手机',\n",
       " '连衣裙',\n",
       " '看着',\n",
       " '绿茵茵',\n",
       " '讲话',\n",
       " '宽阔',\n",
       " '人前',\n",
       " 't台',\n",
       " '、',\n",
       " '唱歌',\n",
       " '沙发',\n",
       " '穿',\n",
       " '绿油油',\n",
       " '长发',\n",
       " '整洁',\n",
       " '碧绿',\n",
       " '浅色',\n",
       " '汽车',\n",
       " '走秀',\n",
       " '展板',\n",
       " '蓝色',\n",
       " '前有',\n",
       " '奔跑',\n",
       " '男孩',\n",
       " '身前',\n",
       " '口袋',\n",
       " '叉腰',\n",
       " '红毯',\n",
       " '兜',\n",
       " '球拍',\n",
       " '高尔夫球',\n",
       " '球杆',\n",
       " '行走',\n",
       " '牵着',\n",
       " '篮球',\n",
       " '人群',\n",
       " '挎着',\n",
       " '地站',\n",
       " '打网球',\n",
       " '篮球场',\n",
       " '腿',\n",
       " '扎',\n",
       " '身后',\n",
       " '地',\n",
       " '外',\n",
       " '工作',\n",
       " '做',\n",
       " '中间',\n",
       " '躺',\n",
       " '沙滩',\n",
       " '看',\n",
       " '跪',\n",
       " '披',\n",
       " '面带笑容',\n",
       " '草坪',\n",
       " '干活',\n",
       " '抓',\n",
       " '旁站',\n",
       " '海边',\n",
       " '手',\n",
       " '搭',\n",
       " '老人',\n",
       " '大',\n",
       " '灯光',\n",
       " '牛仔裤',\n",
       " '马路上',\n",
       " '房屋内',\n",
       " '摸',\n",
       " '抬着',\n",
       " '跳舞',\n",
       " '休闲',\n",
       " '双臂',\n",
       " '棚里',\n",
       " '长袖',\n",
       " '地上',\n",
       " '手套',\n",
       " '下围棋',\n",
       " '边',\n",
       " '扶',\n",
       " '右肩',\n",
       " '床上',\n",
       " '台阶',\n",
       " '张开',\n",
       " '两位',\n",
       " '栏杆',\n",
       " '排球',\n",
       " '头盔',\n",
       " '头',\n",
       " '弯着腰',\n",
       " '前站',\n",
       " '雪地',\n",
       " '杆',\n",
       " '食物',\n",
       " '人来人往',\n",
       " '后面',\n",
       " '墙边',\n",
       " '节目',\n",
       " '办公室',\n",
       " '举着',\n",
       " '下',\n",
       " '黄色',\n",
       " '厨房',\n",
       " '板前',\n",
       " '擂台',\n",
       " '绚丽',\n",
       " '内',\n",
       " '门口',\n",
       " '背景',\n",
       " '短发',\n",
       " '狗',\n",
       " '商店',\n",
       " '双腿',\n",
       " '长裤',\n",
       " '笔',\n",
       " '空地',\n",
       " '叉',\n",
       " '跑步',\n",
       " '球员',\n",
       " '一只',\n",
       " '挽',\n",
       " '腰',\n",
       " '花',\n",
       " '交叉',\n",
       " '吉他',\n",
       " '撑',\n",
       " '衬衫',\n",
       " '奖杯',\n",
       " '玩',\n",
       " '跑',\n",
       " '滑冰',\n",
       " '绿草如茵',\n",
       " '羽毛球',\n",
       " '杯子',\n",
       " '颜色',\n",
       " '呐喊',\n",
       " '滑雪',\n",
       " '橄榄球',\n",
       " '握手',\n",
       " '树林',\n",
       " '昏暗',\n",
       " '小女孩',\n",
       " '共同',\n",
       " '滑冰场',\n",
       " '教室',\n",
       " '左肩',\n",
       " '推',\n",
       " '道',\n",
       " '灰色',\n",
       " '屋外',\n",
       " '骑',\n",
       " '健身房',\n",
       " '男女',\n",
       " '路上',\n",
       " '倚靠在',\n",
       " '玩耍',\n",
       " '比',\n",
       " '骑马',\n",
       " '西服',\n",
       " '打拳击',\n",
       " '翘着',\n",
       " '上身',\n",
       " '自行车',\n",
       " '绿色',\n",
       " '相同',\n",
       " '运动衣',\n",
       " '路边',\n",
       " '一对',\n",
       " '二郎腿',\n",
       " '背心',\n",
       " '凳子',\n",
       " '相机',\n",
       " '阳光',\n",
       " '摘',\n",
       " '左臂',\n",
       " '翠绿',\n",
       " '口罩',\n",
       " '餐厅',\n",
       " '右臂',\n",
       " '戴',\n",
       " '时尚',\n",
       " '长',\n",
       " '举起',\n",
       " '手拿着',\n",
       " '写字',\n",
       " '拥抱',\n",
       " '走廊',\n",
       " '腾空',\n",
       " '袋子',\n",
       " '相互',\n",
       " '田地',\n",
       " '运动装',\n",
       " '土地',\n",
       " '广场',\n",
       " '高尔夫球场',\n",
       " '短',\n",
       " '拍照',\n",
       " '人身',\n",
       " '条纹',\n",
       " '护栏',\n",
       " '房子',\n",
       " '一起',\n",
       " '看书',\n",
       " '吃',\n",
       " '跑道',\n",
       " '海滩',\n",
       " '赤裸',\n",
       " '剪刀手',\n",
       " '棒球',\n",
       " '长裙',\n",
       " '伞',\n",
       " '打电话',\n",
       " '帮',\n",
       " '靠',\n",
       " '拉着',\n",
       " '单手',\n",
       " '比基尼',\n",
       " '整理',\n",
       " '踢球',\n",
       " '保龄球',\n",
       " '地面',\n",
       " '中',\n",
       " '微笑',\n",
       " '手臂',\n",
       " '冰面',\n",
       " '单',\n",
       " '抢球',\n",
       " '同一个',\n",
       " '体育场',\n",
       " '工具',\n",
       " '湖边',\n",
       " '光亮',\n",
       " '病房',\n",
       " '侧身',\n",
       " '行李箱',\n",
       " '低着头',\n",
       " '手牵着',\n",
       " '五个',\n",
       " '石头',\n",
       " '格子',\n",
       " '背',\n",
       " '着手',\n",
       " '玩偶',\n",
       " '平整',\n",
       " '小男孩',\n",
       " '蔬菜',\n",
       " '歪着头',\n",
       " '抬',\n",
       " '书',\n",
       " '斜',\n",
       " '女生',\n",
       " '围裙',\n",
       " '商场',\n",
       " '房间内',\n",
       " '握拳',\n",
       " '车子',\n",
       " '泳池',\n",
       " '交流',\n",
       " '鞋子',\n",
       " '打着',\n",
       " '工作服',\n",
       " '背景墙',\n",
       " '互相',\n",
       " '单膝',\n",
       " '侧',\n",
       " '粉色',\n",
       " '马场',\n",
       " '端',\n",
       " '跃起',\n",
       " '球',\n",
       " '庆祝',\n",
       " '裤兜',\n",
       " '亮堂',\n",
       " '花样滑冰',\n",
       " '系着',\n",
       " '服',\n",
       " '电脑',\n",
       " '果园',\n",
       " '地毯',\n",
       " '围巾',\n",
       " '手握着',\n",
       " '比赛',\n",
       " '古装',\n",
       " '身旁',\n",
       " '安全帽',\n",
       " '制服',\n",
       " '工厂',\n",
       " '大拇指',\n",
       " '趴在',\n",
       " '台上',\n",
       " '小',\n",
       " '胸前',\n",
       " '制作',\n",
       " '戴帽子',\n",
       " '下巴',\n",
       " '捧',\n",
       " '漂亮',\n",
       " '机器',\n",
       " '马甲',\n",
       " '短裙',\n",
       " '手表',\n",
       " '院子',\n",
       " '牌子',\n",
       " '操场上',\n",
       " '医院',\n",
       " '洒满',\n",
       " '公园',\n",
       " '阳光明媚',\n",
       " '瓶子',\n",
       " '打乒乓球',\n",
       " '右腿',\n",
       " '辫子',\n",
       " '网球拍',\n",
       " '船上',\n",
       " '拳套',\n",
       " '戴眼镜',\n",
       " '拳击',\n",
       " '左腿',\n",
       " '屋子',\n",
       " '另',\n",
       " '挥',\n",
       " '扇子',\n",
       " '会议室',\n",
       " '台球',\n",
       " '楼梯',\n",
       " '河边',\n",
       " '肩上',\n",
       " '三位',\n",
       " '泳衣',\n",
       " '模特',\n",
       " '路旁',\n",
       " '外有',\n",
       " '双肩包',\n",
       " '敞亮',\n",
       " '运动',\n",
       " '右',\n",
       " '男',\n",
       " '幕布',\n",
       " '大衣',\n",
       " '地板',\n",
       " '挥杆',\n",
       " '亲吻',\n",
       " '水果',\n",
       " '采访',\n",
       " '环抱',\n",
       " '机舱',\n",
       " '饮料',\n",
       " '阳台',\n",
       " '采摘',\n",
       " '头上',\n",
       " '弯曲',\n",
       " '门前',\n",
       " '杂乱',\n",
       " '打球',\n",
       " '纸',\n",
       " '车厢',\n",
       " '拉',\n",
       " '弹',\n",
       " '超市',\n",
       " '耳机',\n",
       " '托着',\n",
       " '玩具',\n",
       " '旁坐',\n",
       " '同',\n",
       " '讲台',\n",
       " '递',\n",
       " '草丛',\n",
       " '挎包',\n",
       " '拎包',\n",
       " '进行',\n",
       " '麦克风',\n",
       " '面前',\n",
       " '提',\n",
       " '实验室',\n",
       " '锻炼',\n",
       " '握',\n",
       " '提着',\n",
       " '车间',\n",
       " '乐器',\n",
       " '手上',\n",
       " '天空',\n",
       " '马',\n",
       " '白茫茫',\n",
       " '得体',\n",
       " '挂',\n",
       " '铺',\n",
       " '植物',\n",
       " '婚纱',\n",
       " '检查',\n",
       " '酒杯',\n",
       " '画画',\n",
       " '湖面',\n",
       " '服饰',\n",
       " '酒吧',\n",
       " '草原',\n",
       " '摔倒',\n",
       " '挑选',\n",
       " '图书馆',\n",
       " '郁郁葱葱',\n",
       " '膝盖',\n",
       " '纸张',\n",
       " '面对面',\n",
       " '带',\n",
       " '单脚',\n",
       " '草莓',\n",
       " '教',\n",
       " '台子上',\n",
       " '马路边',\n",
       " '沙漠',\n",
       " '牛仔',\n",
       " '演讲',\n",
       " '开会',\n",
       " '女子',\n",
       " '包站',\n",
       " '熙攘',\n",
       " '展厅',\n",
       " '茂盛',\n",
       " '吃饭',\n",
       " '健身',\n",
       " '弯着',\n",
       " '左',\n",
       " '摩托车',\n",
       " '保龄球馆',\n",
       " '练习',\n",
       " '沙地',\n",
       " '长椅',\n",
       " '一望无际',\n",
       " '同一',\n",
       " '下象棋',\n",
       " '箱子',\n",
       " '喂',\n",
       " '两名',\n",
       " '篮子',\n",
       " '动物',\n",
       " '溜冰场',\n",
       " '边站',\n",
       " '无袖',\n",
       " '工地',\n",
       " '瑜伽',\n",
       " '骑着',\n",
       " '轮椅',\n",
       " '光线',\n",
       " '花色',\n",
       " '脚',\n",
       " '病床',\n",
       " '击掌',\n",
       " '一辆',\n",
       " '车',\n",
       " '手势',\n",
       " '围着',\n",
       " '身上',\n",
       " '竖',\n",
       " '冰场',\n",
       " '发布会',\n",
       " '菜地',\n",
       " '石台',\n",
       " '水池',\n",
       " '头巾',\n",
       " '跳跃',\n",
       " '朴素',\n",
       " '实验',\n",
       " '合十',\n",
       " '号',\n",
       " '倒',\n",
       " '红地毯',\n",
       " '白大褂',\n",
       " '游泳池',\n",
       " '蛋糕',\n",
       " '花丛',\n",
       " '一个双',\n",
       " '游泳',\n",
       " '柱子',\n",
       " '医生',\n",
       " '鱼',\n",
       " '跳',\n",
       " '赛场',\n",
       " '物品',\n",
       " '聊天',\n",
       " '木板',\n",
       " '国际象棋',\n",
       " '对视',\n",
       " '车上',\n",
       " '背包',\n",
       " '围栏',\n",
       " '球场',\n",
       " '蓝天',\n",
       " '边有',\n",
       " '秀',\n",
       " '热闹',\n",
       " '白雪皑皑',\n",
       " '一条',\n",
       " '牵着手',\n",
       " '灌篮',\n",
       " '拍摄',\n",
       " '嘴',\n",
       " '锻炼身体',\n",
       " '桌球',\n",
       " '婴儿车',\n",
       " '茂密',\n",
       " '服装',\n",
       " '馆里',\n",
       " '小孩子',\n",
       " '可爱',\n",
       " '一个男孩',\n",
       " '道具',\n",
       " '紫色',\n",
       " '棚子',\n",
       " '大人',\n",
       " '脸',\n",
       " '玩游戏',\n",
       " '伸着',\n",
       " '领带',\n",
       " '扛着',\n",
       " '鼓掌',\n",
       " '欢呼',\n",
       " '体育馆',\n",
       " '餐桌',\n",
       " '场里',\n",
       " '脖子',\n",
       " '田野',\n",
       " '帐篷',\n",
       " '屋内',\n",
       " '眼睛',\n",
       " '冰球',\n",
       " '洁净',\n",
       " '单肩',\n",
       " '空旷',\n",
       " '理发店',\n",
       " '高跟鞋',\n",
       " '演出服',\n",
       " '一匹',\n",
       " '签名',\n",
       " '扔',\n",
       " '石阶',\n",
       " '胸',\n",
       " '清澈',\n",
       " '墙壁',\n",
       " '拍',\n",
       " '墙上',\n",
       " '扭着',\n",
       " '商品',\n",
       " '右脚',\n",
       " '夹',\n",
       " '充足',\n",
       " '飞机',\n",
       " '洒',\n",
       " '河水',\n",
       " '广告',\n",
       " '脸颊',\n",
       " '插进',\n",
       " '水里',\n",
       " '救生衣',\n",
       " '单腿',\n",
       " '大树',\n",
       " '本子',\n",
       " '筷子',\n",
       " '破旧',\n",
       " '垫子',\n",
       " '训练',\n",
       " '菜园',\n",
       " '肩膀',\n",
       " '盘子',\n",
       " '半',\n",
       " '窗户',\n",
       " '盘着',\n",
       " '左脚',\n",
       " '低头',\n",
       " '鲜花',\n",
       " '跟',\n",
       " '菜',\n",
       " '碗',\n",
       " '布前',\n",
       " '男生',\n",
       " '喝水',\n",
       " '起',\n",
       " '手牵手',\n",
       " '身体',\n",
       " '观众席',\n",
       " '盒子',\n",
       " '游戏',\n",
       " '投篮',\n",
       " '婴儿',\n",
       " '刀',\n",
       " '踩',\n",
       " '竖起',\n",
       " '棍子',\n",
       " '往',\n",
       " '桌旁',\n",
       " '游乐场',\n",
       " '地里',\n",
       " '璀璨',\n",
       " '披肩',\n",
       " '医护人员',\n",
       " '绳子',\n",
       " '手扶',\n",
       " '靠着',\n",
       " '绿植旁',\n",
       " '摄像机',\n",
       " '按摩',\n",
       " '向',\n",
       " '划船',\n",
       " '露台',\n",
       " '推车',\n",
       " '店铺',\n",
       " '桶',\n",
       " '草丛里',\n",
       " '笑容满面',\n",
       " '天',\n",
       " '闭着',\n",
       " '笔记本电脑',\n",
       " '琳琅满目',\n",
       " '放',\n",
       " '小道',\n",
       " '各自',\n",
       " '海面',\n",
       " '同样',\n",
       " '笑',\n",
       " '嘴边',\n",
       " '光滑',\n",
       " '树丛',\n",
       " '操作',\n",
       " '拄着',\n",
       " '喝',\n",
       " '双脚',\n",
       " '推着',\n",
       " '弯',\n",
       " '载',\n",
       " '跨栏',\n",
       " '拐杖',\n",
       " '光',\n",
       " '蔚蓝',\n",
       " '帅气',\n",
       " '发言',\n",
       " '牛',\n",
       " '水',\n",
       " '毛笔',\n",
       " '树枝',\n",
       " '垃圾',\n",
       " '冰壶',\n",
       " '一张',\n",
       " '马背上',\n",
       " '指着',\n",
       " '路面',\n",
       " '西瓜',\n",
       " '背后',\n",
       " '理发',\n",
       " '球馆',\n",
       " '勺子',\n",
       " '书架',\n",
       " '统一',\n",
       " '简洁',\n",
       " '一',\n",
       " '男子',\n",
       " '湿漉漉',\n",
       " '海里',\n",
       " '橘色',\n",
       " '擦',\n",
       " '操作电脑',\n",
       " '并排',\n",
       " '台球室',\n",
       " '切',\n",
       " '铁锹',\n",
       " '滑板',\n",
       " '户外',\n",
       " '争',\n",
       " '盘腿',\n",
       " '波光粼粼',\n",
       " '柜台',\n",
       " '耳麦',\n",
       " '睡觉',\n",
       " '潮湿',\n",
       " '杆子',\n",
       " '大褂',\n",
       " '一名',\n",
       " '照耀',\n",
       " '柔软',\n",
       " '护士',\n",
       " '场上',\n",
       " '剪刀',\n",
       " '兜里',\n",
       " '讲解',\n",
       " '烧烤',\n",
       " '伸出',\n",
       " 't恤',\n",
       " '额头',\n",
       " '简陋',\n",
       " '牵',\n",
       " '头顶',\n",
       " '中有',\n",
       " '上篮',\n",
       " '讲课',\n",
       " '绿意盎然',\n",
       " '摆放',\n",
       " '屏幕',\n",
       " '大街',\n",
       " '厨师',\n",
       " '修理',\n",
       " '练',\n",
       " '文件',\n",
       " '溜冰',\n",
       " '撩',\n",
       " '弯腰',\n",
       " '食指',\n",
       " '长凳',\n",
       " '证书',\n",
       " '简朴',\n",
       " '正在',\n",
       " '台',\n",
       " '优雅',\n",
       " '亭子',\n",
       " '生机勃勃',\n",
       " '毛衣',\n",
       " '座椅',\n",
       " '举',\n",
       " '裁判',\n",
       " '橙色',\n",
       " '旗袍',\n",
       " '捡',\n",
       " '弹钢琴',\n",
       " '一头',\n",
       " '柜子',\n",
       " '对',\n",
       " '发箍',\n",
       " '十指',\n",
       " '花束',\n",
       " '照相机',\n",
       " '岩石',\n",
       " '大树下',\n",
       " '卧室',\n",
       " '一件',\n",
       " '间',\n",
       " '跷着',\n",
       " '讨论',\n",
       " '用',\n",
       " '环绕',\n",
       " '洁白',\n",
       " '捂着',\n",
       " '床边',\n",
       " '身材',\n",
       " '裙摆',\n",
       " '皮肤',\n",
       " '海水',\n",
       " '报纸',\n",
       " '托',\n",
       " '台前',\n",
       " '马尾',\n",
       " '照射',\n",
       " '拳击台',\n",
       " '张着',\n",
       " '倒水',\n",
       " '黝黑',\n",
       " '摆满',\n",
       " '平板',\n",
       " '外站',\n",
       " '写',\n",
       " '书店',\n",
       " '趴着',\n",
       " '网球场',\n",
       " '演奏',\n",
       " '滑雪场',\n",
       " '浇水',\n",
       " '接吻',\n",
       " '指导',\n",
       " '抚摸',\n",
       " '弹着',\n",
       " '垃圾场',\n",
       " '使用',\n",
       " '骑着马',\n",
       " '暗淡',\n",
       " '手站',\n",
       " '坐满',\n",
       " '切菜',\n",
       " '人站',\n",
       " '人手',\n",
       " '交叉着',\n",
       " '书本',\n",
       " '下车',\n",
       " '草帽',\n",
       " '棕色',\n",
       " '桥',\n",
       " '亮起',\n",
       " '骑车',\n",
       " '车水马龙',\n",
       " '腰站',\n",
       " '手工',\n",
       " '观众',\n",
       " '羽毛球拍',\n",
       " '漂流',\n",
       " '酒',\n",
       " '赤着',\n",
       " '毛巾',\n",
       " '挥手',\n",
       " '嘴巴',\n",
       " '做饭',\n",
       " '雨伞',\n",
       " '跳绳',\n",
       " '羊',\n",
       " '病人',\n",
       " '灭火',\n",
       " '滑梯',\n",
       " '披着',\n",
       " '小提琴',\n",
       " '争球',\n",
       " '万众瞩目',\n",
       " '玻璃',\n",
       " '水面',\n",
       " '交叠',\n",
       " '转头',\n",
       " '西装革履',\n",
       " '西红柿',\n",
       " '红领巾',\n",
       " '枝繁叶茂',\n",
       " '挑着',\n",
       " '头饰',\n",
       " '耳朵',\n",
       " '旗子',\n",
       " '扶手',\n",
       " '房门',\n",
       " '逗',\n",
       " '花坛',\n",
       " '肚子',\n",
       " '皮艇',\n",
       " '水边',\n",
       " '棋盘',\n",
       " '摊位',\n",
       " '鸽子',\n",
       " '闪烁',\n",
       " '绿树',\n",
       " '电梯',\n",
       " '池塘',\n",
       " '比划',\n",
       " '模型',\n",
       " '树下',\n",
       " '捏',\n",
       " '山坡',\n",
       " '展示',\n",
       " '小狗',\n",
       " '体操',\n",
       " '下棋',\n",
       " '铁轨',\n",
       " '购物车',\n",
       " '货架',\n",
       " '被',\n",
       " '温馨',\n",
       " '武术',\n",
       " '头戴',\n",
       " '剪纸',\n",
       " '钢琴',\n",
       " '自拍',\n",
       " '背上',\n",
       " '笔直',\n",
       " '画',\n",
       " '一块',\n",
       " '阶梯',\n",
       " '身子',\n",
       " '跆拳道',\n",
       " '缰绳',\n",
       " '猫',\n",
       " '整齐',\n",
       " '准备',\n",
       " '绿树成荫',\n",
       " '玩具车',\n",
       " '气球',\n",
       " '杖',\n",
       " '打拳',\n",
       " '围棋',\n",
       " '压腿',\n",
       " '做菜',\n",
       " '仰着',\n",
       " '马上',\n",
       " '调酒',\n",
       " '落叶',\n",
       " '花园里',\n",
       " '舞蹈',\n",
       " '玩电脑',\n",
       " '湍急',\n",
       " '清理',\n",
       " '和服',\n",
       " '雕塑',\n",
       " '脸上',\n",
       " '灭火器',\n",
       " '河里',\n",
       " '斜挎包',\n",
       " '手术室',\n",
       " '宽敞明亮',\n",
       " '几个',\n",
       " '人山人海',\n",
       " '钓鱼',\n",
       " '狭窄',\n",
       " '熙熙攘攘',\n",
       " '游泳馆',\n",
       " '游泳圈',\n",
       " ...]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看所有词汇\n",
    "vectorization.get_vocabulary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 25), dtype=int64, numpy=\n",
       "array([[   5,    8,   10,   42, 2804, 8118,    1,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0]])>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试一下，后面0代表补白\n",
    "vectorization(['一个 男人 穿着 衣服 直升机 好 恩培'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 制作数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "zjxvG3dUIURA"
   },
   "outputs": [],
   "source": [
    "\n",
    "def decode_and_resize(img_path):\n",
    "    # 读取图片，并缩放\n",
    "    img = tf.io.read_file(img_path)\n",
    "    img = tf.image.decode_jpeg(img, channels=3)\n",
    "    img = tf.image.resize(img, IMAGE_SIZE)\n",
    "    img = tf.image.convert_image_dtype(img, tf.float32)\n",
    "    return img\n",
    "\n",
    "\n",
    "def process_input(img_path, captions):\n",
    "    return decode_and_resize(img_path), vectorization(captions)\n",
    "\n",
    "\n",
    "def make_dataset(images, captions):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((images, captions))\n",
    "    dataset = dataset.shuffle(len(images))\n",
    "    dataset = dataset.map(process_input, num_parallel_calls=AUTOTUNE)\n",
    "    dataset = dataset.batch(BATCH_SIZE).prefetch(AUTOTUNE)\n",
    "\n",
    "    return dataset\n",
    "\n",
    "# 制作数据集\n",
    "train_dataset = make_dataset(list(train_data.keys()), list(train_data.values()))\n",
    "valid_dataset = make_dataset(list(valid_data.keys()), list(valid_data.values()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PrefetchDataset shapes: ((None, 299, 299, 3), (None, None, 25)), types: (tf.float32, tf.int64)>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab = np.array(vectorization.get_vocabulary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vocab[[1,2,3,4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# vocab[[1,2,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-08 22:50:50.526665: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2022-09-08 22:50:50.532674: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2000000000 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['<start>' '绿油油' '的' '球场上' '三个' '男人' '的' '中间' '走' '着' '一个' '右手' '比' '着'\n",
      "  '剪刀手' '的' '男人' '<end>' '' '' '' '' '' '' '']\n",
      " ['<start>' '一个' '人' '的' '前面' '有' '三个' '穿着' '短裤' '的' '球员' '在' '宽阔' '的'\n",
      "  '球场上' '踢足球' '<end>' '' '' '' '' '' '' '' '']\n",
      " ['<start>' '两个' '人前' '有' '两个' '穿着' '短袖' '上衣' '的' '男人' '在' '运动场' '上'\n",
      "  '踢足球' '<end>' '' '' '' '' '' '' '' '' '' '']\n",
      " ['<start>' '四个' '穿着' '运动服' '的' '男人' '在' '翠绿' '的' '草地' '上' '踢足球' '<end>'\n",
      "  '' '' '' '' '' '' '' '' '' '' '' '']\n",
      " ['<start>' '四个' '穿着' '运动衣' '的' '人' '在' '平坦' '的' '运动场' '上' '踢足球' '<end>'\n",
      "  '' '' '' '' '' '' '' '' '' '' '' '']]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in train_dataset:\n",
    "    # 获取图片\n",
    "    img = i[0][0].numpy().astype('int')\n",
    "    # 获取标注\n",
    "    caption = i[1][0].numpy()\n",
    "    # 显示\n",
    "    plt.imshow(img)\n",
    "    # 解码\n",
    "    print(vocab[caption])\n",
    "    \n",
    "    \n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建模型\n",
    "# 1.CNN提取图片特征\n",
    "# 2.transformer encoder：输入图片特征，得到新的表达方式\n",
    "# 3.transformer decoder：将encoder的输出、以及文本标注的输入作为输入，然后输出标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cnn_model():\n",
    "    # CNN模型\n",
    "    base_model = efficientnet.EfficientNetB0(\n",
    "        input_shape=(*IMAGE_SIZE, 3), include_top=False, weights=\"imagenet\",\n",
    "    )\n",
    "    # 冻住特征提取层\n",
    "    base_model.trainable = False\n",
    "    base_model_out = base_model.output\n",
    "    base_model_out = layers.Reshape((-1, base_model_out.shape[-1]))(base_model_out)\n",
    "    cnn_model = keras.models.Model(base_model.input, base_model_out)\n",
    "    return cnn_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model = get_cnn_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 299, 299, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "rescaling (Rescaling)           (None, 299, 299, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "normalization (Normalization)   (None, 299, 299, 3)  7           rescaling[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "stem_conv_pad (ZeroPadding2D)   (None, 301, 301, 3)  0           normalization[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "stem_conv (Conv2D)              (None, 150, 150, 32) 864         stem_conv_pad[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "stem_bn (BatchNormalization)    (None, 150, 150, 32) 128         stem_conv[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "stem_activation (Activation)    (None, 150, 150, 32) 0           stem_bn[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "block1a_dwconv (DepthwiseConv2D (None, 150, 150, 32) 288         stem_activation[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block1a_bn (BatchNormalization) (None, 150, 150, 32) 128         block1a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block1a_activation (Activation) (None, 150, 150, 32) 0           block1a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_squeeze (GlobalAvera (None, 32)           0           block1a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_reshape (Reshape)    (None, 1, 1, 32)     0           block1a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_reduce (Conv2D)      (None, 1, 1, 8)      264         block1a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_expand (Conv2D)      (None, 1, 1, 32)     288         block1a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_excite (Multiply)    (None, 150, 150, 32) 0           block1a_activation[0][0]         \n",
      "                                                                 block1a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1a_project_conv (Conv2D)   (None, 150, 150, 16) 512         block1a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1a_project_bn (BatchNormal (None, 150, 150, 16) 64          block1a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2a_expand_conv (Conv2D)    (None, 150, 150, 96) 1536        block1a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_expand_bn (BatchNormali (None, 150, 150, 96) 384         block2a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2a_expand_activation (Acti (None, 150, 150, 96) 0           block2a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_dwconv_pad (ZeroPadding (None, 151, 151, 96) 0           block2a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2a_dwconv (DepthwiseConv2D (None, 75, 75, 96)   864         block2a_dwconv_pad[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_bn (BatchNormalization) (None, 75, 75, 96)   384         block2a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2a_activation (Activation) (None, 75, 75, 96)   0           block2a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_squeeze (GlobalAvera (None, 96)           0           block2a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_reshape (Reshape)    (None, 1, 1, 96)     0           block2a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_reduce (Conv2D)      (None, 1, 1, 4)      388         block2a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_expand (Conv2D)      (None, 1, 1, 96)     480         block2a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_excite (Multiply)    (None, 75, 75, 96)   0           block2a_activation[0][0]         \n",
      "                                                                 block2a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_project_conv (Conv2D)   (None, 75, 75, 24)   2304        block2a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_project_bn (BatchNormal (None, 75, 75, 24)   96          block2a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2b_expand_conv (Conv2D)    (None, 75, 75, 144)  3456        block2a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_expand_bn (BatchNormali (None, 75, 75, 144)  576         block2b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2b_expand_activation (Acti (None, 75, 75, 144)  0           block2b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_dwconv (DepthwiseConv2D (None, 75, 75, 144)  1296        block2b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2b_bn (BatchNormalization) (None, 75, 75, 144)  576         block2b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2b_activation (Activation) (None, 75, 75, 144)  0           block2b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_squeeze (GlobalAvera (None, 144)          0           block2b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_reshape (Reshape)    (None, 1, 1, 144)    0           block2b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block2b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block2b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_excite (Multiply)    (None, 75, 75, 144)  0           block2b_activation[0][0]         \n",
      "                                                                 block2b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_project_conv (Conv2D)   (None, 75, 75, 24)   3456        block2b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_project_bn (BatchNormal (None, 75, 75, 24)   96          block2b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2b_drop (Dropout)          (None, 75, 75, 24)   0           block2b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_add (Add)               (None, 75, 75, 24)   0           block2b_drop[0][0]               \n",
      "                                                                 block2a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_expand_conv (Conv2D)    (None, 75, 75, 144)  3456        block2b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3a_expand_bn (BatchNormali (None, 75, 75, 144)  576         block3a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3a_expand_activation (Acti (None, 75, 75, 144)  0           block3a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_dwconv_pad (ZeroPadding (None, 79, 79, 144)  0           block3a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3a_dwconv (DepthwiseConv2D (None, 38, 38, 144)  3600        block3a_dwconv_pad[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_bn (BatchNormalization) (None, 38, 38, 144)  576         block3a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3a_activation (Activation) (None, 38, 38, 144)  0           block3a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_squeeze (GlobalAvera (None, 144)          0           block3a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_reshape (Reshape)    (None, 1, 1, 144)    0           block3a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block3a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block3a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_excite (Multiply)    (None, 38, 38, 144)  0           block3a_activation[0][0]         \n",
      "                                                                 block3a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_project_conv (Conv2D)   (None, 38, 38, 40)   5760        block3a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_project_bn (BatchNormal (None, 38, 38, 40)   160         block3a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3b_expand_conv (Conv2D)    (None, 38, 38, 240)  9600        block3a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_expand_bn (BatchNormali (None, 38, 38, 240)  960         block3b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3b_expand_activation (Acti (None, 38, 38, 240)  0           block3b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_dwconv (DepthwiseConv2D (None, 38, 38, 240)  6000        block3b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3b_bn (BatchNormalization) (None, 38, 38, 240)  960         block3b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3b_activation (Activation) (None, 38, 38, 240)  0           block3b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_squeeze (GlobalAvera (None, 240)          0           block3b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_reshape (Reshape)    (None, 1, 1, 240)    0           block3b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block3b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block3b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_excite (Multiply)    (None, 38, 38, 240)  0           block3b_activation[0][0]         \n",
      "                                                                 block3b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_project_conv (Conv2D)   (None, 38, 38, 40)   9600        block3b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_project_bn (BatchNormal (None, 38, 38, 40)   160         block3b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3b_drop (Dropout)          (None, 38, 38, 40)   0           block3b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_add (Add)               (None, 38, 38, 40)   0           block3b_drop[0][0]               \n",
      "                                                                 block3a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_expand_conv (Conv2D)    (None, 38, 38, 240)  9600        block3b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4a_expand_bn (BatchNormali (None, 38, 38, 240)  960         block4a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4a_expand_activation (Acti (None, 38, 38, 240)  0           block4a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_dwconv_pad (ZeroPadding (None, 39, 39, 240)  0           block4a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4a_dwconv (DepthwiseConv2D (None, 19, 19, 240)  2160        block4a_dwconv_pad[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_bn (BatchNormalization) (None, 19, 19, 240)  960         block4a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4a_activation (Activation) (None, 19, 19, 240)  0           block4a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_squeeze (GlobalAvera (None, 240)          0           block4a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_reshape (Reshape)    (None, 1, 1, 240)    0           block4a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block4a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block4a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_excite (Multiply)    (None, 19, 19, 240)  0           block4a_activation[0][0]         \n",
      "                                                                 block4a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_project_conv (Conv2D)   (None, 19, 19, 80)   19200       block4a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_project_bn (BatchNormal (None, 19, 19, 80)   320         block4a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4b_expand_conv (Conv2D)    (None, 19, 19, 480)  38400       block4a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_expand_bn (BatchNormali (None, 19, 19, 480)  1920        block4b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4b_expand_activation (Acti (None, 19, 19, 480)  0           block4b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_dwconv (DepthwiseConv2D (None, 19, 19, 480)  4320        block4b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4b_bn (BatchNormalization) (None, 19, 19, 480)  1920        block4b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4b_activation (Activation) (None, 19, 19, 480)  0           block4b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_squeeze (GlobalAvera (None, 480)          0           block4b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_excite (Multiply)    (None, 19, 19, 480)  0           block4b_activation[0][0]         \n",
      "                                                                 block4b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_project_conv (Conv2D)   (None, 19, 19, 80)   38400       block4b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_project_bn (BatchNormal (None, 19, 19, 80)   320         block4b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4b_drop (Dropout)          (None, 19, 19, 80)   0           block4b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_add (Add)               (None, 19, 19, 80)   0           block4b_drop[0][0]               \n",
      "                                                                 block4a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_expand_conv (Conv2D)    (None, 19, 19, 480)  38400       block4b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4c_expand_bn (BatchNormali (None, 19, 19, 480)  1920        block4c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4c_expand_activation (Acti (None, 19, 19, 480)  0           block4c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_dwconv (DepthwiseConv2D (None, 19, 19, 480)  4320        block4c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4c_bn (BatchNormalization) (None, 19, 19, 480)  1920        block4c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4c_activation (Activation) (None, 19, 19, 480)  0           block4c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_squeeze (GlobalAvera (None, 480)          0           block4c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_excite (Multiply)    (None, 19, 19, 480)  0           block4c_activation[0][0]         \n",
      "                                                                 block4c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_project_conv (Conv2D)   (None, 19, 19, 80)   38400       block4c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_project_bn (BatchNormal (None, 19, 19, 80)   320         block4c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4c_drop (Dropout)          (None, 19, 19, 80)   0           block4c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_add (Add)               (None, 19, 19, 80)   0           block4c_drop[0][0]               \n",
      "                                                                 block4b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5a_expand_conv (Conv2D)    (None, 19, 19, 480)  38400       block4c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5a_expand_bn (BatchNormali (None, 19, 19, 480)  1920        block5a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5a_expand_activation (Acti (None, 19, 19, 480)  0           block5a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_dwconv (DepthwiseConv2D (None, 19, 19, 480)  12000       block5a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5a_bn (BatchNormalization) (None, 19, 19, 480)  1920        block5a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5a_activation (Activation) (None, 19, 19, 480)  0           block5a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_squeeze (GlobalAvera (None, 480)          0           block5a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_reshape (Reshape)    (None, 1, 1, 480)    0           block5a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block5a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block5a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_excite (Multiply)    (None, 19, 19, 480)  0           block5a_activation[0][0]         \n",
      "                                                                 block5a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_project_conv (Conv2D)   (None, 19, 19, 112)  53760       block5a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_project_bn (BatchNormal (None, 19, 19, 112)  448         block5a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5b_expand_conv (Conv2D)    (None, 19, 19, 672)  75264       block5a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_expand_bn (BatchNormali (None, 19, 19, 672)  2688        block5b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5b_expand_activation (Acti (None, 19, 19, 672)  0           block5b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_dwconv (DepthwiseConv2D (None, 19, 19, 672)  16800       block5b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5b_bn (BatchNormalization) (None, 19, 19, 672)  2688        block5b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5b_activation (Activation) (None, 19, 19, 672)  0           block5b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_squeeze (GlobalAvera (None, 672)          0           block5b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_excite (Multiply)    (None, 19, 19, 672)  0           block5b_activation[0][0]         \n",
      "                                                                 block5b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_project_conv (Conv2D)   (None, 19, 19, 112)  75264       block5b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_project_bn (BatchNormal (None, 19, 19, 112)  448         block5b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5b_drop (Dropout)          (None, 19, 19, 112)  0           block5b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_add (Add)               (None, 19, 19, 112)  0           block5b_drop[0][0]               \n",
      "                                                                 block5a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_expand_conv (Conv2D)    (None, 19, 19, 672)  75264       block5b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5c_expand_bn (BatchNormali (None, 19, 19, 672)  2688        block5c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5c_expand_activation (Acti (None, 19, 19, 672)  0           block5c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_dwconv (DepthwiseConv2D (None, 19, 19, 672)  16800       block5c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5c_bn (BatchNormalization) (None, 19, 19, 672)  2688        block5c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5c_activation (Activation) (None, 19, 19, 672)  0           block5c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_squeeze (GlobalAvera (None, 672)          0           block5c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_excite (Multiply)    (None, 19, 19, 672)  0           block5c_activation[0][0]         \n",
      "                                                                 block5c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_project_conv (Conv2D)   (None, 19, 19, 112)  75264       block5c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_project_bn (BatchNormal (None, 19, 19, 112)  448         block5c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5c_drop (Dropout)          (None, 19, 19, 112)  0           block5c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_add (Add)               (None, 19, 19, 112)  0           block5c_drop[0][0]               \n",
      "                                                                 block5b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6a_expand_conv (Conv2D)    (None, 19, 19, 672)  75264       block5c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6a_expand_bn (BatchNormali (None, 19, 19, 672)  2688        block6a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6a_expand_activation (Acti (None, 19, 19, 672)  0           block6a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_dwconv_pad (ZeroPadding (None, 23, 23, 672)  0           block6a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6a_dwconv (DepthwiseConv2D (None, 10, 10, 672)  16800       block6a_dwconv_pad[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6a_bn (BatchNormalization) (None, 10, 10, 672)  2688        block6a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6a_activation (Activation) (None, 10, 10, 672)  0           block6a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_squeeze (GlobalAvera (None, 672)          0           block6a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_reshape (Reshape)    (None, 1, 1, 672)    0           block6a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block6a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block6a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_excite (Multiply)    (None, 10, 10, 672)  0           block6a_activation[0][0]         \n",
      "                                                                 block6a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_project_conv (Conv2D)   (None, 10, 10, 192)  129024      block6a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_project_bn (BatchNormal (None, 10, 10, 192)  768         block6a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6b_expand_conv (Conv2D)    (None, 10, 10, 1152) 221184      block6a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_expand_bn (BatchNormali (None, 10, 10, 1152) 4608        block6b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6b_expand_activation (Acti (None, 10, 10, 1152) 0           block6b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_dwconv (DepthwiseConv2D (None, 10, 10, 1152) 28800       block6b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6b_bn (BatchNormalization) (None, 10, 10, 1152) 4608        block6b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6b_activation (Activation) (None, 10, 10, 1152) 0           block6b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_squeeze (GlobalAvera (None, 1152)         0           block6b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_excite (Multiply)    (None, 10, 10, 1152) 0           block6b_activation[0][0]         \n",
      "                                                                 block6b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_project_conv (Conv2D)   (None, 10, 10, 192)  221184      block6b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_project_bn (BatchNormal (None, 10, 10, 192)  768         block6b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6b_drop (Dropout)          (None, 10, 10, 192)  0           block6b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_add (Add)               (None, 10, 10, 192)  0           block6b_drop[0][0]               \n",
      "                                                                 block6a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_expand_conv (Conv2D)    (None, 10, 10, 1152) 221184      block6b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6c_expand_bn (BatchNormali (None, 10, 10, 1152) 4608        block6c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6c_expand_activation (Acti (None, 10, 10, 1152) 0           block6c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_dwconv (DepthwiseConv2D (None, 10, 10, 1152) 28800       block6c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6c_bn (BatchNormalization) (None, 10, 10, 1152) 4608        block6c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6c_activation (Activation) (None, 10, 10, 1152) 0           block6c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_squeeze (GlobalAvera (None, 1152)         0           block6c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_excite (Multiply)    (None, 10, 10, 1152) 0           block6c_activation[0][0]         \n",
      "                                                                 block6c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_project_conv (Conv2D)   (None, 10, 10, 192)  221184      block6c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_project_bn (BatchNormal (None, 10, 10, 192)  768         block6c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6c_drop (Dropout)          (None, 10, 10, 192)  0           block6c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_add (Add)               (None, 10, 10, 192)  0           block6c_drop[0][0]               \n",
      "                                                                 block6b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6d_expand_conv (Conv2D)    (None, 10, 10, 1152) 221184      block6c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6d_expand_bn (BatchNormali (None, 10, 10, 1152) 4608        block6d_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6d_expand_activation (Acti (None, 10, 10, 1152) 0           block6d_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_dwconv (DepthwiseConv2D (None, 10, 10, 1152) 28800       block6d_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6d_bn (BatchNormalization) (None, 10, 10, 1152) 4608        block6d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6d_activation (Activation) (None, 10, 10, 1152) 0           block6d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_squeeze (GlobalAvera (None, 1152)         0           block6d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_excite (Multiply)    (None, 10, 10, 1152) 0           block6d_activation[0][0]         \n",
      "                                                                 block6d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_project_conv (Conv2D)   (None, 10, 10, 192)  221184      block6d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_project_bn (BatchNormal (None, 10, 10, 192)  768         block6d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6d_drop (Dropout)          (None, 10, 10, 192)  0           block6d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_add (Add)               (None, 10, 10, 192)  0           block6d_drop[0][0]               \n",
      "                                                                 block6c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block7a_expand_conv (Conv2D)    (None, 10, 10, 1152) 221184      block6d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block7a_expand_bn (BatchNormali (None, 10, 10, 1152) 4608        block7a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7a_expand_activation (Acti (None, 10, 10, 1152) 0           block7a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_dwconv (DepthwiseConv2D (None, 10, 10, 1152) 10368       block7a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block7a_bn (BatchNormalization) (None, 10, 10, 1152) 4608        block7a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block7a_activation (Activation) (None, 10, 10, 1152) 0           block7a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_squeeze (GlobalAvera (None, 1152)         0           block7a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block7a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block7a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block7a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_excite (Multiply)    (None, 10, 10, 1152) 0           block7a_activation[0][0]         \n",
      "                                                                 block7a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_project_conv (Conv2D)   (None, 10, 10, 320)  368640      block7a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_project_bn (BatchNormal (None, 10, 10, 320)  1280        block7a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "top_conv (Conv2D)               (None, 10, 10, 1280) 409600      block7a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "top_bn (BatchNormalization)     (None, 10, 10, 1280) 5120        top_conv[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "top_activation (Activation)     (None, 10, 10, 1280) 0           top_bn[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, 100, 1280)    0           top_activation[0][0]             \n",
      "==================================================================================================\n",
      "Total params: 4,049,571\n",
      "Trainable params: 0\n",
      "Non-trainable params: 4,049,571\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-08 22:50:53.398075: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n",
      "2022-09-08 22:50:54.299279: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8101\n",
      "2022-09-08 22:50:55.472615: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n",
      "2022-09-08 22:50:56.210023: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n",
      "2022-09-08 22:50:56.216809: I tensorflow/stream_executor/cuda/cuda_blas.cc:1838] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
     ]
    }
   ],
   "source": [
    "# 模拟图片测试一下\n",
    "cnn_test_input = tf.random.normal([64, 299,299,3])\n",
    "# 输入网络\n",
    "cnn_test_output = cnn_model(cnn_test_input, training=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([64, 100, 1280])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看大小\n",
    "cnn_test_output.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TransformerEncoderBlock(layers.Layer):\n",
    "    # transformer encoder网络：https://www.youtube.com/watch?v=n9TlOhRjYoc\n",
    "    def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):\n",
    "        super().__init__()\n",
    "        self.embed_dim = embed_dim\n",
    "        self.dense_dim = dense_dim\n",
    "        self.num_heads = num_heads\n",
    "        self.attention_1 = layers.MultiHeadAttention(\n",
    "            num_heads=num_heads, key_dim=embed_dim, dropout=0.0\n",
    "        )\n",
    "        self.layernorm_1 = layers.LayerNormalization()\n",
    "        self.layernorm_2 = layers.LayerNormalization()\n",
    "        self.dense_1 = layers.Dense(embed_dim, activation=\"relu\")\n",
    "\n",
    "    def call(self, inputs, training, mask=None):\n",
    "        # layer norm\n",
    "        inputs = self.layernorm_1(inputs)\n",
    "        \n",
    "        inputs = self.dense_1(inputs)\n",
    "        # multi head attention\n",
    "        # training：布尔值，表示推理还是训练（是否使用 dropout）\n",
    "        attention_output_1 = self.attention_1(\n",
    "            query=inputs,\n",
    "            value=inputs,\n",
    "            key=inputs,\n",
    "            attention_mask=None,\n",
    "            training=training,\n",
    "        )\n",
    "        # residual然后再layer norm\n",
    "        out_1 = self.layernorm_2(inputs + attention_output_1)\n",
    "        return out_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试一下\n",
    "encoder = TransformerEncoderBlock(embed_dim=EMBED_DIM, dense_dim=FF_DIM, num_heads=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 输入网络\n",
    "encoder_test_output = encoder(cnn_test_output, training=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([64, 100, 512])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder_test_output.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionalEmbedding(layers.Layer):\n",
    "    # 位置编码\n",
    "    def __init__(self, sequence_length, vocab_size, embed_dim, **kwargs):\n",
    "        super().__init__()\n",
    "        '''\n",
    "        embedding用法：https://stats.stackexchange.com/questions/270546/how-does-keras-embedding-layer-work\n",
    "        input_dim：词汇数量；output_dim：特征向量大小\n",
    "        将下图左列转为右边\n",
    "        +------------+------------+\n",
    "        |   index    |  Embedding |\n",
    "        +------------+------------+\n",
    "        |     0      | [1.2, 3.1] |\n",
    "        |     1      | [0.1, 4.2] |\n",
    "        |     2      | [1.0, 3.1] |\n",
    "        |     3      | [0.3, 2.1] |\n",
    "        |     4      | [2.2, 1.4] |\n",
    "        |     5      | [0.7, 1.7] |\n",
    "        |     6      | [4.1, 2.0] |\n",
    "        +------------+------------+\n",
    "        '''\n",
    "        # token embedding：长度为vocab_size，特征向量为：embed_dim\n",
    "        self.token_embeddings = layers.Embedding(\n",
    "            input_dim=vocab_size, output_dim=embed_dim\n",
    "        )\n",
    "        # position_embeddings：\n",
    "        self.position_embeddings = layers.Embedding(\n",
    "            input_dim=sequence_length, output_dim=embed_dim\n",
    "        )\n",
    "        self.sequence_length = sequence_length\n",
    "        self.vocab_size = vocab_size\n",
    "        self.embed_dim = embed_dim\n",
    "\n",
    "        # 512开根号：22.627416998：https://jalammar.github.io/illustrated-transformer/\n",
    "        self.embed_scale = tf.math.sqrt(tf.cast(embed_dim, tf.float32))\n",
    "\n",
    "\n",
    "    def call(self, inputs):\n",
    "        # 获取caption长度，这里是24个（前24个单词）\n",
    "        length = tf.shape(inputs)[-1]\n",
    "\n",
    "        # 生成0~length的数字\n",
    "        positions = tf.range(start=0, limit=length, delta=1)\n",
    "        \n",
    "        # 输入的句子index转为embedding特征，大小：(N, 24, 512)\n",
    "        embedded_tokens = self.token_embeddings(inputs)\n",
    "        # 乘以22.62\n",
    "        embedded_tokens = embedded_tokens * self.embed_scale\n",
    "        \n",
    "        # 位置编码，大小：(24, 512)\n",
    "        embedded_positions = self.position_embeddings(positions)\n",
    "        \n",
    "        # 加和 返回\n",
    "        return embedded_tokens + embedded_positions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试模型\n",
    "test_embedding_model = PositionalEmbedding(embed_dim=EMBED_DIM, sequence_length=SEQ_LENGTH, vocab_size=VOCAB_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 24)\n",
      "(64, 24, 512)\n"
     ]
    }
   ],
   "source": [
    "# 测试输入，选择一个batch中的第一个句子（一共有5个）\n",
    "for i in train_dataset:\n",
    "    # print(i[1].shape)\n",
    "    \n",
    "    # 获取测试标签,大小：(64, 24)，前24个词\n",
    "    caption = i[1][:,0,:-1]\n",
    "    print(caption.shape)\n",
    "    \n",
    "    # 传入模型\n",
    "    positional_output = test_embedding_model(caption)\n",
    "    # 打印结果的大小\n",
    "    print(positional_output.shape)\n",
    "    \n",
    "    \n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 可以看到，将长度为24的单词变为512的embedding向量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 解码器\n",
    "class TransformerDecoderBlock(layers.Layer):\n",
    "    \n",
    "    def __init__(self, embed_dim, ff_dim, num_heads, **kwargs):\n",
    "        super().__init__()\n",
    "        self.embed_dim = embed_dim\n",
    "        self.ff_dim = ff_dim\n",
    "        self.num_heads = num_heads\n",
    "        self.attention_1 = layers.MultiHeadAttention(\n",
    "            num_heads=num_heads, key_dim=embed_dim, dropout=0.1\n",
    "        )\n",
    "        self.attention_2 = layers.MultiHeadAttention(\n",
    "            num_heads=num_heads, key_dim=embed_dim, dropout=0.1\n",
    "        )\n",
    "        self.ffn_layer_1 = layers.Dense(ff_dim, activation=\"relu\")\n",
    "        self.ffn_layer_2 = layers.Dense(embed_dim)\n",
    "\n",
    "        self.layernorm_1 = layers.LayerNormalization()\n",
    "        self.layernorm_2 = layers.LayerNormalization()\n",
    "        self.layernorm_3 = layers.LayerNormalization()\n",
    "        \n",
    "        # 位置编码\n",
    "        self.embedding = PositionalEmbedding(\n",
    "            embed_dim=EMBED_DIM, sequence_length=SEQ_LENGTH, vocab_size=VOCAB_SIZE\n",
    "        )\n",
    "        \n",
    "        self.out = layers.Dense(VOCAB_SIZE, activation=\"softmax\")\n",
    "\n",
    "        self.dropout_1 = layers.Dropout(0.3)\n",
    "        self.dropout_2 = layers.Dropout(0.5)\n",
    "        self.supports_masking = True\n",
    "\n",
    "    def call(self, inputs, encoder_outputs, training, mask=None):\n",
    "        \n",
    "        # 获取位置编码，(N,24) --> (N,24,512)\n",
    "        inputs = self.embedding(inputs)\n",
    "        \n",
    "        '''\n",
    "        shape:(64,24,24)\n",
    "        64个一模一样，大小为(24, 24)的mask\n",
    "        \n",
    "        [[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0]\n",
    "         [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]]\n",
    "         \n",
    "        '''\n",
    "        causal_mask = self.get_causal_attention_mask(inputs)\n",
    "        \n",
    "        \n",
    "        '''\n",
    "        mask (64,24) --> padding_mask (64, 24, 1)\n",
    "\n",
    "        64个大小为(24, 1)的mask\n",
    "\n",
    "        [[1][1][1]...[0][0][0][0][0]]\n",
    "\n",
    "        '''\n",
    "        padding_mask = tf.cast(mask[:, :, tf.newaxis], dtype=tf.int32)\n",
    "\n",
    "        '''\n",
    "        mask (64,24) --> combined_mask (64, 1, 24)            \n",
    "        64个大小为(1, 24)的mask\n",
    "        [[1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n",
    "\n",
    "        '''\n",
    "        combined_mask = tf.cast(mask[:, tf.newaxis, :], dtype=tf.int32)\n",
    "\n",
    "\n",
    "        '''\n",
    "        在combined_mask与causal_mask选择最小值，大小(64, 24, 24)\n",
    "        64个不再一模一样，大小为(24, 24)的mask\n",
    "\n",
    "        [[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
    "         [1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n",
    "\n",
    "        '''\n",
    "\n",
    "        combined_mask = tf.minimum(combined_mask, causal_mask)\n",
    "     \n",
    "            \n",
    "        # 第一个masked self  attention，QKV都是inputs, mask是causal mask，强制训练时只关注输出位置左侧的token，以便模型可以自回归地推断\n",
    "        \n",
    "        attention_output_1 = self.attention_1(\n",
    "            query=inputs,\n",
    "            value=inputs,\n",
    "            key=inputs,\n",
    "            attention_mask=combined_mask,\n",
    "            training=training,\n",
    "        )\n",
    "        out_1 = self.layernorm_1(inputs + attention_output_1)\n",
    "        \n",
    "\n",
    "        \n",
    "        # cross attention，其中K、V来自encoder，Q来自decoder前一个的attention输出，mask是padding mask，用来遮挡25个单词中补白的部分\n",
    "        attention_output_2 = self.attention_2(\n",
    "            query=out_1,\n",
    "            value=encoder_outputs,\n",
    "            key=encoder_outputs,\n",
    "            attention_mask=padding_mask,\n",
    "            training=training,\n",
    "        )\n",
    "        out_2 = self.layernorm_2(out_1 + attention_output_2)\n",
    "\n",
    "        ffn_out = self.ffn_layer_1(out_2)\n",
    "        ffn_out = self.dropout_1(ffn_out, training=training)\n",
    "        ffn_out = self.ffn_layer_2(ffn_out)\n",
    "\n",
    "        ffn_out = self.layernorm_3(ffn_out + out_2, training=training)\n",
    "        ffn_out = self.dropout_2(ffn_out, training=training)\n",
    "        \n",
    "        # 最后输出为VOCAB_SIZE大小的向量，对应位置的大小为概率，可以查索引来获取相应原单词\n",
    "        preds = self.out(ffn_out)\n",
    "        return preds\n",
    "\n",
    "    def get_causal_attention_mask(self, inputs):\n",
    "        '''\n",
    "        causal: 因果关系mask\n",
    "        '''\n",
    "        # (N,24,512)\n",
    "        input_shape = tf.shape(inputs)\n",
    "        # 分别为N，24\n",
    "        batch_size, sequence_length = input_shape[0], input_shape[1]\n",
    "\n",
    "        #范围0~24的列表，变成大小(24, 1)的数组\n",
    "        i = tf.range(sequence_length)[:, tf.newaxis]\n",
    "        #范围0~24的列表\n",
    "        j = tf.range(sequence_length)\n",
    "        mask = tf.cast(i >= j, dtype=\"int32\")\n",
    "        \n",
    "        # 大小为(1, 24, 24)\n",
    "        mask = tf.reshape(mask, (1, input_shape[1], input_shape[1]))\n",
    "        \n",
    "        scale = tf.concat(\n",
    "            [tf.expand_dims(batch_size, -1), tf.constant([1, 1], dtype=tf.int32)],\n",
    "            axis=0,\n",
    "        )\n",
    "        # (1, 24, 24)铺成(64, 24, 24)\n",
    "        result = tf.tile(mask, scale)\n",
    "\n",
    "        return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试模型\n",
    "decoder = TransformerDecoderBlock(embed_dim=EMBED_DIM, ff_dim=FF_DIM, num_heads=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# decoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 24, 10000)\n"
     ]
    }
   ],
   "source": [
    "# 测试输入\n",
    "\n",
    "for i in train_dataset:\n",
    "    # 前0~ -1（24）个单词（去尾）\n",
    "    batch_seq_inp = i[1][:,0,:-1]\n",
    "    # print(batch_seq_inp.shape)\n",
    "\n",
    "    # 前1~ 个（24）个单词（掐头），用做ground truth标注\n",
    "    batch_seq_true = i[1][:,0,1:]\n",
    "    # print(batch_seq_true.shape)\n",
    "    \n",
    "    # 将batch_seq_true中的每一个元素和0作对比，返回类似[true,true,false]形式的mask，遇到0，则会变成false，0表示字符串中长度不够25的补白部分（padding）\n",
    "    mask = tf.math.not_equal(batch_seq_true, 0)\n",
    "    # print(mask.shape)\n",
    "    \n",
    "\n",
    "    # 输入decoder预测的序列\n",
    "    batch_seq_pred = decoder(\n",
    "        batch_seq_inp, encoder_test_output, training=False, mask=mask\n",
    "    )\n",
    "    \n",
    "    print(batch_seq_pred.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 数据增强\n",
    "image_augmentation = keras.Sequential(\n",
    "    [\n",
    "        layers.experimental.preprocessing.RandomFlip(\"horizontal\"),\n",
    "        layers.experimental.preprocessing.RandomRotation(0.2),\n",
    "        layers.experimental.preprocessing.RandomContrast(0.3),\n",
    "    ]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aZq0umUiIURB",
    "outputId": "96f0f7ee-af45-4d62-eae6-3f2817a25046"
   },
   "outputs": [],
   "source": [
    "class ImageCaptioningModel(keras.Model):\n",
    "    def __init__(\n",
    "        self, cnn_model, encoder, decoder, num_captions_per_image=5, image_aug=None,\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self.cnn_model = cnn_model\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.loss_tracker = keras.metrics.Mean(name=\"loss\")\n",
    "        self.acc_tracker = keras.metrics.Mean(name=\"accuracy\")\n",
    "        self.num_captions_per_image = num_captions_per_image\n",
    "        self.image_aug = image_aug\n",
    "\n",
    "    def calculate_loss(self, y_true, y_pred, mask):\n",
    "        loss = self.loss(y_true, y_pred)\n",
    "        mask = tf.cast(mask, dtype=loss.dtype)\n",
    "        loss *= mask\n",
    "        return tf.reduce_sum(loss) / tf.reduce_sum(mask)\n",
    "\n",
    "    def calculate_accuracy(self, y_true, y_pred, mask):\n",
    "        accuracy = tf.equal(y_true, tf.argmax(y_pred, axis=2))\n",
    "        accuracy = tf.math.logical_and(mask, accuracy)\n",
    "        accuracy = tf.cast(accuracy, dtype=tf.float32)\n",
    "        mask = tf.cast(mask, dtype=tf.float32)\n",
    "        return tf.reduce_sum(accuracy) / tf.reduce_sum(mask)\n",
    "\n",
    "    def _compute_caption_loss_and_acc(self, img_embed, batch_seq, training=True):\n",
    "        '''\n",
    "        计算loss\n",
    "        '''\n",
    "        \n",
    "        # 图片的embedding特征输入encoder,得到新的seq，大小（N,100,512）\n",
    "        encoder_out = self.encoder(img_embed, training=training)\n",
    "        \n",
    "        # batch_seq的shape：(64, 25)\n",
    "        # 前24个单词（去尾）\n",
    "        batch_seq_inp = batch_seq[:, :-1]\n",
    "        \n",
    "        # 后24个单词（掐头），用做ground truth标注\n",
    "        batch_seq_true = batch_seq[:, 1:]\n",
    "        \n",
    "        # mask掩码，将batch_seq_true中的每一个元素和0作对比，返回类似[true,true,false]形式的mask，遇到0，则会变成false，0表示字符串中长度不够25的补白部分（padding）\n",
    "        mask = tf.math.not_equal(batch_seq_true, 0)\n",
    "        \n",
    "        # 输入decoder预测的序列\n",
    "        batch_seq_pred = self.decoder(\n",
    "            batch_seq_inp, encoder_out, training=training, mask=mask\n",
    "        )\n",
    "        # 计算loss和acc\n",
    "        loss = self.calculate_loss(batch_seq_true, batch_seq_pred, mask)\n",
    "        acc = self.calculate_accuracy(batch_seq_true, batch_seq_pred, mask)\n",
    "        return loss, acc\n",
    "\n",
    "    def train_step(self, batch_data):\n",
    "        '''\n",
    "        训练步骤\n",
    "        '''\n",
    "        # 获取图片和标注\n",
    "        batch_img, batch_seq = batch_data\n",
    "        # 初始化\n",
    "        batch_loss = 0\n",
    "        batch_acc = 0\n",
    "        # 是否使用数据增强\n",
    "        if self.image_aug:\n",
    "            batch_img = self.image_aug(batch_img)\n",
    "\n",
    "        # 获取图片embedding特征\n",
    "        img_embed = self.cnn_model(batch_img)\n",
    "\n",
    "        # 遍历5个文本标注\n",
    "        for i in range(self.num_captions_per_image):\n",
    "            with tf.GradientTape() as tape:\n",
    "                # 计算loss和acc\n",
    "                # batch_seq的shape：(64, 5, 25)\n",
    "                loss, acc = self._compute_caption_loss_and_acc(\n",
    "                    img_embed, batch_seq[:, i, :], training=True\n",
    "                )\n",
    "\n",
    "                # 更新loss和acc\n",
    "                batch_loss += loss\n",
    "                batch_acc += acc\n",
    "\n",
    "            # 获取所有可训练参数\n",
    "            train_vars = (\n",
    "                self.encoder.trainable_variables + self.decoder.trainable_variables\n",
    "            )\n",
    "\n",
    "            # 获取梯度\n",
    "            grads = tape.gradient(loss, train_vars)\n",
    "\n",
    "            # 更新参数\n",
    "            self.optimizer.apply_gradients(zip(grads, train_vars))\n",
    "\n",
    "        # 更新\n",
    "        batch_acc /= float(self.num_captions_per_image)\n",
    "        self.loss_tracker.update_state(batch_loss)\n",
    "        self.acc_tracker.update_state(batch_acc)\n",
    "\n",
    "        return {\"loss\": self.loss_tracker.result(), \"acc\": self.acc_tracker.result()}\n",
    "\n",
    "    def test_step(self, batch_data):\n",
    "        batch_img, batch_seq = batch_data\n",
    "        batch_loss = 0\n",
    "        batch_acc = 0\n",
    "\n",
    "        # 获取图片embedding特征\n",
    "        img_embed = self.cnn_model(batch_img)\n",
    "\n",
    "        # 遍历5个文本标注\n",
    "        for i in range(self.num_captions_per_image):\n",
    "            \n",
    "            loss, acc = self._compute_caption_loss_and_acc(\n",
    "                img_embed, batch_seq[:, i, :], training=False\n",
    "            )\n",
    "\n",
    "            batch_loss += loss\n",
    "            batch_acc += acc\n",
    "\n",
    "        batch_acc /= float(self.num_captions_per_image)\n",
    "\n",
    "        self.loss_tracker.update_state(batch_loss)\n",
    "        self.acc_tracker.update_state(batch_acc)\n",
    "\n",
    "        return {\"loss\": self.loss_tracker.result(), \"acc\": self.acc_tracker.result()}\n",
    "\n",
    "    @property\n",
    "    def metrics(self):\n",
    "        return [self.loss_tracker, self.acc_tracker]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型实例化\n",
    "cnn_model = get_cnn_model()\n",
    "encoder = TransformerEncoderBlock(embed_dim=EMBED_DIM, dense_dim=FF_DIM, num_heads=1)\n",
    "decoder = TransformerDecoderBlock(embed_dim=EMBED_DIM, ff_dim=FF_DIM, num_heads=2)\n",
    "caption_model = ImageCaptioningModel(\n",
    "    cnn_model=cnn_model, encoder=encoder, decoder=decoder, image_aug=image_augmentation,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# encoder.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zUedRANwIURD",
    "outputId": "b8b6b944-1142-4d59-f56e-9b1b64a219e8"
   },
   "outputs": [],
   "source": [
    "\n",
    "# loss\n",
    "cross_entropy = keras.losses.SparseCategoricalCrossentropy(\n",
    "    from_logits=False, reduction=\"none\"\n",
    ")\n",
    "\n",
    "# 提前终止\n",
    "early_stopping = keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True)\n",
    "\n",
    "\n",
    "class LRSchedule(keras.optimizers.schedules.LearningRateSchedule):\n",
    "    def __init__(self, post_warmup_learning_rate, warmup_steps):\n",
    "        super().__init__()\n",
    "        self.post_warmup_learning_rate = post_warmup_learning_rate\n",
    "        self.warmup_steps = warmup_steps\n",
    "\n",
    "    def __call__(self, step):\n",
    "        global_step = tf.cast(step, tf.float32)\n",
    "        warmup_steps = tf.cast(self.warmup_steps, tf.float32)\n",
    "        warmup_progress = global_step / warmup_steps\n",
    "        warmup_learning_rate = self.post_warmup_learning_rate * warmup_progress\n",
    "        return tf.cond(\n",
    "            global_step < warmup_steps,\n",
    "            lambda: warmup_learning_rate,\n",
    "            lambda: self.post_warmup_learning_rate,\n",
    "        )\n",
    "\n",
    "\n",
    "# LR调节\n",
    "num_train_steps = len(train_dataset) * EPOCHS\n",
    "num_warmup_steps = num_train_steps // 15\n",
    "lr_schedule = LRSchedule(post_warmup_learning_rate=1e-4, warmup_steps=num_warmup_steps)\n",
    "\n",
    "# 编译\n",
    "caption_model.compile(optimizer=keras.optimizers.Adam(lr_schedule), loss=cross_entropy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 训练\n",
    "# caption_model.fit(\n",
    "#     train_dataset,\n",
    "#     epochs=EPOCHS,\n",
    "#     validation_data=valid_dataset,\n",
    "#     callbacks=[early_stopping],\n",
    "# )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载权重\n",
    "load_status = caption_model.load_weights(\"./my_model/checkpoint\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab = vectorization.get_vocabulary()\n",
    "index_lookup = dict(zip(range(len(vocab)), vocab))\n",
    "max_decoded_sentence_length = SEQ_LENGTH - 1\n",
    "valid_images = list(valid_data.keys())\n",
    "valid_caption = list(valid_data.values())\n",
    "valid_len = len(valid_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 827
    },
    "id": "IzgAfngvIURE",
    "outputId": "2c9a1539-5d03-47bd-b2ad-f1a0637027f6"
   },
   "outputs": [],
   "source": [
    "# 测试\n",
    "import random\n",
    "def generate_caption():\n",
    "    # 在测试集中随机取一张图片\n",
    "    random_index = random.randrange(0,valid_len)\n",
    "    sample_img = valid_images[random_index]\n",
    "    sample_caption = valid_caption[random_index][0]\n",
    "    # 读取图片\n",
    "    sample_img = decode_and_resize(sample_img)\n",
    "    img_show = sample_img.numpy().clip(0, 255).astype(np.uint8)\n",
    "\n",
    "    plt.imshow(img_show)\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "    \n",
    "    # 保存\n",
    "    cv2.imwrite('./img/raw.jpg',cv2.cvtColor(img_show,cv2.COLOR_RGB2BGR))\n",
    "    # 获取CNN特征\n",
    "    img = tf.expand_dims(sample_img, 0)\n",
    "    img = caption_model.cnn_model(img)\n",
    "\n",
    "    # 传给encoder\n",
    "    encoded_img = caption_model.encoder(img, training=False)\n",
    "    \n",
    "    \n",
    "    \n",
    "    # 1.先提供\"<start> \"\n",
    "    # 2.传给decoder推理，\n",
    "    # 3.不断投喂给模型，直到遇到<end>停止\n",
    "    # 4.如果循环次数超出句子长度，也停止\n",
    "    decoded_caption = \"<start> \"\n",
    "    for i in range(max_decoded_sentence_length):\n",
    "        tokenized_caption = vectorization([decoded_caption])[:, :-1]\n",
    "        mask = tf.math.not_equal(tokenized_caption, 0)\n",
    "\n",
    "        # 预测\n",
    "        predictions = caption_model.decoder(\n",
    "            tokenized_caption, encoded_img, training=False, mask=mask\n",
    "        )\n",
    "        sampled_token_index = np.argmax(predictions[0, i, :])\n",
    "        sampled_token = index_lookup[sampled_token_index]\n",
    "        if sampled_token == \" <end>\":\n",
    "            break\n",
    "        decoded_caption += \" \" + sampled_token\n",
    "\n",
    "    decoded_caption = decoded_caption.replace(\"<start> \", \"\")\n",
    "    decoded_caption = decoded_caption.replace(\" <end>\", \"\").strip()\n",
    "    \n",
    "    sample_caption = sample_caption.replace(\"<start> \", \"\")\n",
    "    sample_caption = sample_caption.replace(\" <end>\", \"\").strip()\n",
    "    \n",
    "    \n",
    "    print(\"预测: \", decoded_caption)\n",
    "    print('真实：',sample_caption)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "_kABPzbAMc3c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9V5ClW3bfif22+cyxedJnVWV5d31f3wbt0A0QQMMThiBFkSFyQuS8ScEnRShCEzFP86IHKRTSaEITnKEIgBiQcANDtEGj7e2+fb2pW96l98d/Zhs97O9kVd2+t9qAo+FD74qszDx5zGf22nut//qv/xLee34yfjJ+Mv7zG/J/7QP4yfjJ+Mn44PET4/zJ+Mn4z3T8xDh/Mn4y/jMdPzHOn4yfjP9Mx0+M8yfjJ+M/06Ef9sfRyutejPsMN25T7G+QYBkNuuxt77C5ssrB/gDhIlwp2TvoMxqPWZpt067B4qmTnPzIk0RTHYyXlIXl7o07fPNr32avm4FMkR6UK1G+5MLJGZ78yKMsnL9I48hxdGsKmdTwzoHzCFdg8y4+66OsgdJRjDKcMMStKURrARE3EFIjsPhihNvZwO7dpejtQqNDvHQC3ZmFKEHKGgiBswacBTx4jwdEUkM021Bv4WUMIkWK6lIJj0fgseBKhBkjyhzf7+FGA2xvB5XvYIZ9aMyhF08j0yZepGA94mCdtde/xu2rbzC3MMvc0lHiRhsbt0imjxBPLyKSJkRp+Dib47MBbriPGx0gTI7Nh2BKhKohrMCVBdHUDGL2KGtX3mVvb4tzz36Mzc1tblx5j/XNHe5sdLl+d4180OUzzz7KFz7/SY4eP84oB92cRk81IYrBgSgNZneTjctvUpcwtXQcGjWIFAqNcAIjQdRqiLiG0DFeRuA9uBJMCXiEThG1GiQxaI2XGuEVHgVCI4TAI5FK4QG8ACQC8X1z8YfJKgjxAa87/MmF4/MO4T0ID9ZT5mO+9dp3+L0//lM2t/qkIuKf/Pav8fmf/hRRUg/HKAEhwheAl4gfYl/7Qcc8OV7xQQfODzBOdIJROf3CUYxKSpfjRgVuNCaVgghPXhR4q2gkikQlSBw6qoOMcE5QZgaHwGYGbYDSMy4KjPYo50iKghOdFsvzM0y1aiQKcCXelHipwAGWYKClxBmF9w7vLUWZgy1QOkZFIxAalEd4C6XDORFuuANXGGxmkJlB+hivfbh11uGdBefAOxwCIQ3KWLxzOEBIj/cgBXgxuSkSZz3COjAGkRe4LMeMMxiPKQcjpMhQ4xwvI5wSeOuRzlBrNZhbXKQ91SZKGwgVUxYldjBA1ztIlSIwYWYZg88NLispxwXKlfjSUJYlQimkFbgyhyxDZTm9Xo8b125y5OyjXL16iTdefZmDgyHrOyPubPfQOLb3uoxGGb4o8aVHmDHepAgU3kkoDc46rIPMlDSKHBErPBoBSA9WCqQpUWhwgnBZBN5asKYyCoMwBrTCAUhRPS4Q0j044X+I8bDJ/iHz+8PeKNzbwjAejtne3mI8HmGtQ8YSYwx5kaN0glIKIST+R3n/hxzzj3KcDzVOkTSIpWbm2Gl2RkNcf49iXEBZkipFo1YnyzJK55BKkAho1TRxmuCBLDfU6xGRThHOYd0OSE1hLE4INJ66huXZKZbmOjQaKUmaIKTCe4FAIaREeAFonDA4XxJusUFIj/IgvQ/P8QohNA6JV55SJUgZI4REOBBeAhFORCilER6880gBzpUIL6u5IvEecCC1QggVXuurxTdcZaRUIBUIhZQShwAP0gWjlaVFG18ZvQcESEWU1mlMzZA2m6i0hYpidGGwJixKWI8XILxAeInyEqiMwxuQCitleI4ME9x7hwTaU9PMzi+SpAlFPuL8mROUhefYgWF6bZfu/i71RpPCgREKIUrwHmH9gzuD0gitcYXBOY/0E8/C4V1lZM7f+yJ8OWsR1eRHebz3CA9CyHvG6AVChK8f1jh/JOP7kOGrcxCAJxzbOBuzt7dPkReAoF6r02q1wvFWZ/V3+eS/y3E/1DilSFBRRHNqieSMY//mJYY7G5TWsnfQpz/y9DOLsVCLBI00ptaISVOJlw6rJKLehHiKpF3jbGeBstbAfu1b7G5skvqShak6M/MtWrNTxI0mIkpwOgFdQ0QNhIyCW4FH5jFSCtzQ4L0F4YIhyhgZNyp3sIb3Hq8KfD2n6GosIKUApZBJA9I6TkskIJXAlWW4YcbgvUCiCO6VwodnAWEiCeDefxKEQkgNSoYdQoD1DiEEzrlqcmrwmnAiKSpuEdemEXENogZSSpTweGcRzh66d14KQOGUAh0howiPwXuJFAJb2YRzbjITUDKh1xuC8LRbNbw24GLiWFCWlj1RYPIR47yk9BIf1XBCo5xEuHvnJJUGqTDe46qrcGhY937EVTY3edBJgXQCJ8LV4/6NQxBc1h9xvgohwnX8Oxuov3ccvnpPIMsyirJEyZS0ltJo1NFa31tAfszxYa/9YYk/D985JyueiogbLabmFxjs3MYVTegO2dvaZ/cgp15v0GokpDWPjgVSh6gskjGxqqPSNjKqAU2eeO4FGkmdt775TQZrd1icadNemELUE3yS4qIULyQSB1LgdIxX4USFjElkhMVj+hkQJoFTGq80TmmkThBCoqKEurQUxRG6wy6REKiw+eHQaCERSoLU4X2cRYhw3HiH9NzbJiuXzQlZTazwvLCqKoTS+FijajWitI3tbeKEwWPxKgaZBFdVKfCGuLNAlmegQAgFUiKFruIYiRAeLz1eqMoLmBhoiNmclbjD54JCVMcrmJqdRyd1rPEIIRkOxygMZT/n7OIs548tUms2iGpN9vs5Ub2JyiHRjkYtDguUNVgn8CqlIMMKifSSSEbgJE6GCSaRWCmDIR/ujPLQHr1j4mZUcWS1yIW/Hn73rrrIf8fJ/AOHCHfNEdZJEQl0JJmbnSfWKVnuibSiXm8glUbIsEjjRFiMqA7f/+gLzI8zHmqc1pvg0imFSFJErYGstcjZQscptVpKOizptOu0mppmw6O1w5WW7GDA7somtfoS7doMUklK47BC0Z6fZun4PJkcsrQ4RatTQ9djiDVOSpS3KJeBq+FIEUIDOuwkkUAmLeT4AIECX2CLEbYYoWstpCQYkRdImaBaC0SdPnZvBTfsomp9lG4gPKAERvgQRyoZXDvr8KYIwIazSF8BQELgq4knsOH1QiKkDjZca+F8naidIsc9xmYEkcYRIUWKUBFSabxqoaMjtCPJeH8D53L8BGzysrrxAiEVUsWVqy4QzmKLDGyBVJVBe4nyLsSeQqN0im7WOHPxUeqNKZRq4tQUwjqmpxt4meBkxNhKNvfHbA838W6LNK1Rm2ozHF7CFTm1JGKqltBpzzM7v4T0OVpphJM4L/CEHUWJGFSCVBEIFXYiqfE2XJ8QagC+2jGZzHAPwhG8k8r4qkn/n8J9/bARAKh7y6rHojU89/RztKaO8I1vfodEC2pJCl4eek1hPQ4Gyn+ideKHGQ8HhJwPBqFifNwinjlC48hZdrf3KPMNYkpOHp2m02nRbEZIWWDGY7wTSG+5e/UyB8OcC88J2keWEVGCSCL0VJvGkUVaiWWuHdNu10hjhZIS5y04gxmXOAuioZD1FlJXboLXiChFRk3wKZg+dmjJvUYQJilJE4/ESYWP65A0GeaWcnOduhG0dIJodkAFl9QLEDYC5fDkIB1eOKQIO2eIeRXVDKp2OI9gsmNInGhgVIlMPaIxDf1tvLHY8S6y3kAkMV5InErwroFsLaCsw+yvoK3B2xJrSso8wzuLquZvcOsFaAfROKC0foRG4ESwZyfBCTfZuGh02qxs7rDbt+wNJWlUY3DQ4/V3Xydtt8iNYa/bo542OHHkOAuzc3gtGA4G7O/tsrZym7u3bjI/O8OnPv5Rnrx4guWji8zOzpKkNbzUlM6jhUQJkCJ4FUIIUALnPM4VIR72VIi7A+EQiAc3nr9rUPcDh3/gm7i3/YH1SA8z7TbnTkhcUTLoHVCv1VCVR1W9qvpf3P9W/4uPh7u1NkcoEDIGFSHSNgtnHqWmFO8c7OJxzHamaNQS6vUEYwu6xmGMw5UGVxT0tlbZ3bhDOtUiaXZQccz00hFmOi383ia+t4lUGVJFYQV1FuEKhPeUmUeoBKFjkBonQGkJ9SbKzBLlOdnuEFvm0O/ifYSqkGIfJYgkRilF68gpxHjAweoNhmvr+HiKhk7CDVAKISNUXM1sU7ltojJGUcHvwgY39/67LHyFQsoATEUExD5J8T7BjPbJ5V2sTqknKSgdUg5xHS8lolZQdndQ1S7ti4xxd4dmM4QBQioQCV7FGGUoUQhCTKyiBh5BXoxwGHwxgqyHrDeRUrJyd41vfPs1Tp25yHs3bqMQZDJldXWbeqtJLzNs72/SmV5gtLbOd19+6TBWHgyHDIcD1vpDXr1ynSOdJo+cPcmLTz/Oz/7s5+l0ZpBxSl6UJHGMExYvFUJEFfDjqp0xWKH3vrq3HhnWtSre8/fF72H8p4ktHxwOQvrEu3upFGfxxlJkBaPBmNFBl5r0TC8uEqvoA9M5//8eDzVOP9hFtTp47UNuCgkypj67yNFHHyVzGSqSJKlCCcJE8ppSlBhrwOYU/X2ynR3csQFxrYmQKbnWiEaHVMeIRp1ytIdgFOBtYxBSonBgDS4bIqImUqWgBX4SJ9bbRB6MzzEHu8ROIq2HogBbILQCH3Yq0imaR8+gVER3Zw/rJc45pHMBtUPiVRTsDRlWVx9XqZISEAHVDH8IrlG1/AfvVlQuowYdoXxMS7Tpb91kMN6BvQ2iWoNISIg8QmuIEtKpWaQ7SbG3gc/GSFvgRvuMdlbRkUIpgVUSqyKMAhtrdlZ32F25A0YyuzTPzEwNIUtG/X3e+vp1Xn/nOt9++TVKX2NQSl5/6xpJPaHdbrK/32XvoMvMfEjn1JtNbqzcYnN1ja29PUprUVFEVpYMxiNKa5FSs70x5MbW21y9vUJ3lPNbv/UbtKIErMWXJVJGIZUiZYX0gnSTHBiVa3sP0X3/VnkYz/0vsI2+f5cTgHAebxy2KOkddFnfWMcYRzOJqTXqKDnxkv7XHQ81zuz2ezC7SDS9AEmTEBxbrPPESQNb5IAgatYxTmDKEmkdlA7rBM4rxoMh1996A6/g3DMv0ExjnCCgeZFGJgmUMd5YnNB4keBkLQTuJsOPehgf8FNRb+F9HJB7JOgInUwhogJvHaXzDAdDkrSFVgkiOH9YPKLewk3NgxEMrEeMCpqxDQYtHU6KsJsJVd08D6VFCBNiSiGBiTErYJJGEXjh8TrExYIIGglSN5manqWW9TDjAT5KQjrFWYQLsauXEboxizSGbNwHOcLbAjfq44YDnK5jVIIVijfeeJM//6M/5L0rl3jzrTdJXMzHPv5p/jf/8h8yH0dc/s5b/PkXv4ONaowyQy/vsj8yHIwKitKSpinTM1PoWLHXPaBW5uRZzqDXxTtPiaSf5eT9EYWzKK2xhBSSdQ7hLSs7++wNRuzs7dNZPIIdjXGjHCU1FV4cYmUJTsmQepnEnIfeZfjlHizk7zNc+YPjuh8V6eUeoOS9xxuLsBZnSoxzyCRCRgpvDToS1OspUoeY/jD1M9ndf8Bn/yB3d3INHnDpH3JOD3dre5tkJsO5knh6CaQOUD+2SkobLFAIg0zreGmQsUePHVqC8pA4wWjvgPdefZ1BZnnio59kavEoTkcBObUKrZvI2iw+ruF1ihUKa3K87eLyAYODLYbjMY2ZRXR9CiEk0pc4bymjGN9sB8TPQoakGIyJnaaWgNIKLwRGJOipRRq6STYuyL3CjT2Rt6g4QkURKInUDoRFSoX0Ed4pvKnADCFxeIrChhsrPSqS6FiDtCghAr9Fy2DAUZsorqHqHby1YY8WHmkL8A7vQyJeRRGFVuAtwhjMoMtwa5W6jslNyZ/+9Vf5N//D7/O9V99k5C3ohEg2oRdTfP0dnm8Y9l6/zX4puLW5xag/pBSCsYHcWjyaPC8Z7+6ilMcaQ6vZop7WGI4z8rwgs57SWjyglcY5h5IC6wxSRljriestbq/v8Mf/85d4ZmWb55/5CNPNWkhFyWBVUgbXG18BbT64lAFAC/nbw1k6maPe3wfUfrBD+WPHed6DNyHmNQZf5jhTgLVoLI1agnAlthghk4QoUmgN4CoXnQDQ3e9q/zjo8QOG+cOtMA/Pc5oB0miiMkPlI4gShBQo4anFEUcWl1DlEOskQibE9Tp+OiKzXUSeI7ISrTWx0xRWI3SKkwojBDJJkLqOqDXBGoyKcSrGC/DeIcoM6Qk5OFMgpMbkGaqC7R0BZNBRDVGPUU7iLUQWjAMyKGyBTBK81hihsbIG9SZR7DHeUwJWKJTXaK9RXgXD0g4pJFpptFTI6sY4MYHhPc5YiqKE3BAnhjiN8So8VxKAEYTHeo/14H0JJifyLqC9zoAL+VqBReLxtoQiQ/mMLBuRZ0M2S8urX/1rXn/zHYxIET7jI4+c4cLZRzh2/hEG3R0u3V7n7vouW3sjdns5Y6fJrMEBUsrgCXiDLVwVRnvKbo+Dbh+tVJUxCrFeQCYdWkrqtRrNRovufp9Gs81+P+OdK3dZXd0mqbV5/KlnmE7rjMsCTdh5hPL3vNiQK5pEA4ffDzGZyaR9YN4/OPH/U8SfwrvgsdiA7Lt8RCxFyBVLiCUMygJb5ggsWovgoU84GQ8xxg/92w94ePK6cM0/+BwfbpzSVlzJAmEtRFWuT2lilVCvtRkZj0pnSBfO0Gh1AE+0u8fG+gpjucrI7jPGcPzxj3D2hU/QPnMaXa/jtcJLCQ6Mrah2qMBEsSVOgdUOazXBjEC4iDL3CBGcKCkUOIl3itLpCnkVgWYnBaVSARwSEVaGBDlS4CNxyAARMiT7nZi4Mr5Cagluq5L3VvUKihfeo7yp+HwFWqmw08rAFJq8wDuH1DpQ7EwJJsGbHLB4X+JMgbMhX2tlYC5JAkUPwI77yHzM2vWrmNKCTnnm0Yv8X//rf8UzF85w58Yd/vT3/4SvXruKnW4zvrOBzEqckuH4hQhIqZ8kgAARDBYvKJ2jsBVAosKCp7QgUjEL83MM+gMSLWk1U7SOyb1j7KAhNHv9AXc2Nlm+eB5dFPiiwDqPtQ4vPdIL8CVKiAqM8YG3e8hNruL3B3ZRX4FH/J0JAPfeVeCErkB1iayYVXiHQGIKy3CUsXfQw0cp04CP4grgEmHhus+QftAQh/lx/3326fh+Y5ZSfug++lDjdELinEdbDz6QlJEaH8WUtZRRvUE8PcfxR18gXjyLkDHCC5qjMZ3ddQ5WbrF3d43ucMT55z9K5/gpdL2DSnQAUZzFCYvzIRHtfYEpS4yxOOOxVmOFCjuWMUgLygi01milcN7jpIUoCoCM0CADICGEQAqNFQSOrrwHhwc4Xx26LJPEuRUCJQLZRwgwwmN8lVIRIJHIilKotAJhA3aqRGWYGiFlde1ACF25bBKlImSSIlwZuLzWIFyJKHqAIGpl5P0B+WhMYj1COoQ1zLfqXDhznK++vY7Qgn/5T3+HZ0+fo8gMf/b1L9Md7vP00WW6yiGWmux3M5pOMjSGcWkwVgRCAoHALSNFGidIIbFlSZ7nIKDVbOK8Y2FujlhrinFOohVaCpwtqLWaNNtNkihiZ2+b7772PY6eOs5Tn/0s9VqNOHEI7/DOYIocXy3ozjmssSFc8K7K/t+3jU7G4Q5a5RMnC9wPsob7X89kt7sHLwXQTgXCifeoxCO9g3wMSAbjjCs37vDOpUssHjvGkXOP4nVM6SGepFwmH3GfYf1QC8f7DfQDXvOw93mocarmciBtp21clAYuqQPlBUnSZunCi7QWjhFPzyOSGk5KvPWoJKKVKqglJEvLLClF2ujg04RSBdK6L4N/Y43DGhcM1HuMV1gUTkoMVOCRx1U8TSklSimkFIF3K8PqI5W8j78JXghKJUHeW4EPV2PvkRM3bnIzq9jf+UA4mOAA0kuErbgtIvBsweGFwAHOSZyBVFqkF3jhEFKjqrdlUtWAwHkqlpDAewVOI1WCT2ZRyQKt5lGSnbsUmzdguIvyhqRzhvbiWZL4e3zqxQt8+uNP4m1GXUZ00pRioUO200f3CnZ7GSPnycsCY20oEBAgVEhjaCHRQlGPNZHS6HqNbDwGD1NTbTyOUa/LzmhEkiQYU9JO2iwvL+OcZ7rTYXNjjanpadrTTd649C5zf/kX/L3P/gztVhsZK1AeXdZQRYkri0BhjCROSlACoav0VEhwIHBV3rj6XU6okj947n/YONyVK9MId0oFthYCUfGBvcvIyxG3tne5uddDtNpEtZgSR1R5QJOQZpIOen+q5/2x6GHa6PD2vx+Zvve7lA+vbHm4cU4fQ0oNSR2nQ67TCxkOMKnR6cwhGy1EFIPSwWUJuQVQmtrUDGmzg3MhXYEAV5YUZbVbehHcIC+C6wl4FYzDWoFTAlfxH9WhMVWGdp9xAngpD1dKObE0cc8g7zdQKgN9/+PiEJnzh8appETc99lSCIRQOC8Q0pNlGYPhkIPdjHa7RZKkCFGgKndYCHl4nBWsdA8BFBowgUVACr5DrARxvU52sEU27KHjJs8//QSffecy586dZTCwuE4HA2S55Mq122S9MVlWYKxnnBeMSkeBxwhPXhqqKBhlwGMYDIbEOkIrhTUhf7u1sxMqXYQgrdWI0pSIlHFe0B9mLC0usLK2ytz0NN57RuOcgxs3yP74j5mSER/9xMeYPX4UqwVOqzAfkrSq5AmmImWgKPrg3+IP+ct/t8TFgzGsr4yxcp79JK3j8NbjbYEtC7RU4DXjrGBzew8dJdTqDdqtFlqEVBs/wHg+aEzmyQ+Tr/1Bz3m4Wxs3YZI4sEWI9ZTGCY+PUgwS7SXeeYS1lccSWEUiqRHHdawxeGMoshxnDNaXOCcJl1DiUIGAQIyTVXmOJFQ0OHdvZ6tuqJDy8ITCTfHf5wJNbpZ6n2F+0EU8fK9qtZvcj8muKkQwVClCrBuMUyK8RIoEF0GRl2zurrO1tY33ltFogHWWZrPOVLtNp92m3Z5CRRFK61C/eJgjDVbrvcRqhVEpvjaLSlo0yzESx6c+/UnOXjhP2miTO4GMI771t1+ht7lFQycUScnm5gZaSsrcBBDMmrB7VuVOwof8bWk91hmK0la7h0cpEe6ZFERa0xuN6I1GIRcsJEoosrwIhPA0JZaK0XjIlStXyIYD/uOf/TGX336Vf/Gv/g/oqQ5K1/BJEq6t85WnEphEE35riDVFtbtMjKDaPX/EWDM4n4E1KyoUXFJVxliHNwZTFtgix9qSOI4hjrHesdcdMs4McVQnUhGxigPrSaoPnDM/8EhC7uahzz2cp879+MYpZk4iXAmuwMsJaCBx1oIvwSps4VDSILxAycp10wk6ivFeImIHZYEpLePRAGNKjBN4oZE6sGZUlV+s/L9wUUUwFOcrg6h2Xsk9dCsgXYESdpgfExUY/76d8wetUveMNVy+Q8OUoto5J7vuPRfYW4GUmjiu0WxOcXCwg7UWaw0bm2vUajGjUQczngZbUm+0iNIUGcdIHR0CDtI7lLMhBneWvChwtiARFuIYoxNml48jpKPtS0x/lTtvfgO3fhO728NJxfMvPMfm7Q2G2R02h2NKb6u4t+KHenFIAZgwdPBUAInEC491DpPneO/DziHCPmcBNxzhges3b6HwrK+tMzXVIlKSzbu3KQd7rFy9wvmnnw+0OCWrmk3ui7XEPXTy/nsh7h2XOJy6P3jcW6T9vXypCJQ84TxYC6UhHw4Z9Q442N+hNdUiXVjCCU1mcnb3exgXdndnHbYsQ8jjHYjvN9Af6bjuc3E/aBy6yR/y9x9gnEerAy1DvqtyM52xKFOgmYAtIY1hnccLgZIxkyoFgQSnKPOCXveAPB/grEPrBBU1IWlXVSsBaHETqN17pLRYW1I6j3PB8qRUxFVeUssA0QQXZnJRfFXj+ME+//0X5v0Xc/J4QBQrhNNX9Z7+Xo2+xAKiquMO+bykXsMdCIqq/hEJ2/vbWFmQSkjjqFpULdKUREmK0hrvJViDzUtsUWKdR6c1bAZ5lqN8htYgdITHo73A5AWpdxTjXZbm6vz2z/8ScWOG9y5f5xd/4efRSYM/+au/4ivfe5WuDRzRyORMKmD8YXWpCPG9leDcPdaT9wEksw4nq2vvLQd7Y4QSKCmpJymPnjnF5z77At/7xrfJ84ydzS2WByPiNMJaQ6QFQquqgD2kVQLOHkC4++4Gk7j/0KU4fPz+n9wh4BO+TSpgKmOagEI+HK+3BdYUIV0lBda5ypUOQGee5wzHI7wSlMJjpAhxYOnxqsq9fsD08ffNn3vLzr15O/nOA8Yn8BOqRhVWhbKditDyAeOhxhmlqjpZdbgAhvR6uLhhEwn+/OTDwg0OiX9RPW6NwXnQSY1xMWI87uOKA1Q8QNUKdM2i0zgYv60uujNIQnH1aDRi0B9ivSBpNGl3ZknqDWq1BpFShyBPSBxPCq/vGd0PguUfXN3uBf2iApZc9XrnQUqPxVXXN7iDXgiciim9pj82jIZj8hLGpWF/2GcqbVAbDZFK47wjTWvYPEdHoSxJeJDWI1SMSmOckuhGB0YNyr27aHpE9RY+SqtUVsrS8nGuvv1d5jsR507M0pk7yoWFKfbXVhEyZ/mXnqcuDP/hpfcYuRgvNEI6HO6wFjbEoqE6xB9Cm5UZWIuQhJhfKKwLqLVAMN1q8jOf/AzPPfMUX/rm3/DWjbucO7HMpfcuc+zECRaPHkHFEZ4a+CgUTghARIEgj6jmEPfb3wP3oLoxD5qncA/YiveB2IGr6k19ZbbeBzDMlngzRrsyyNyYEkQo8RPIKkNgMNhQeK4lWsdIP6nhvTce9L78vRpaqIrNJ6dThV/e463DO1eVxIEV9tADC7brEGjQ8QfOy4dza21BnufkeYHWmiRJUUrjKvfh0G1k4l7ISsWg4qcKhfAOHSfMLC0ytbhAUeaUoz7jfo/xaMxgmDPsb+K6BlcW2KKknqRoLdGRwjrDcHeXW7dvczAYkrZnWD73GPNLJ/Ao6klcFcbyfSvdw3bHD/r53u9h9ww7tazizsl5htgz5MoCsiiExAoNUZ3+aIuyEOSlwIuELHP0x2Pq2RgVRcgqJ6p1hHeWSLgAmAmFUDE6qWGVxgBCe5KihS4ytNHk3pHbMS4yzC11+Pijj9CaqaNiQ57vM7x0mXh4wPZgn8Io/uFnnuH6jbu8udknT1IKYytASh26kAETt8HoD69VON8kiQMtsnA4Ge5FW0WcXlgkO9jmb7/8Ra5cvcSLzz/Db/zW7/C5z/00kZIILOQ5pQkSMjJOEFEKUR2h4gqdDeSEiTt730xnEns67x68P97eO0bu2+Whuh8Tz9YFDMQYyMcUu5vcvbPC9bubPPlCg+n5RYQOoGJhSvAQCUUap6RJ8j7G0gfMJe8Q3hwa3cQ4fXUynirfay2uMlDnXKgcum8uesArhUqafNB4qHG+8b3vUBQFzolQfIoMOxugIkkUJygdoXVEkqZM9tHW1BS1JCHWCqkCAKB0qK5Popg0rdPszGCLHFOW5FnOzvYGl99+m+7WBnOdDjMz0/g0prCWfNjDjHvs7awzWL/D/nDAReOJkjqxkkgpQmrlg5DZ+y7uZPW7n2t5eMGrv4Wfw64oRFXpJML735sMldRFFbcJIZA6JkkbGCMwpUc6RaoSSjNm9+AApaMw7So0O40hUoLSmyBL4gUq8qRRTBRplKy8k1pKkY/o9w7IywLpxrjigHGvS72R4H2ByIbsrW0idjbZunaHXNc4+ZGnENMN/tU//Txfeu0SX3r1Bpt9Q1GGFV/pGGNDadyE7zpxD4X3KCGhKFAIanFC3xtUHHOk2aKdjznZidkf7vLbf+9Ffvbv/w5PffynEWkHV8m5CDNG2hHCleTDHlJnxA2BiO8VrN+7+pOF4V4KxBhzuDtN7pmZTL777vNEZkbLsKvjBdI7ZFHgioKs22X1+g3efPNd1ns5xy9+hGUvUN5ibY6MNM5apLA04qAZhKry4h/kbPmKyeUsJh/jJiSOSiHCEWJXZy35eMxwMCQbjynLEhVJ4iRGSYm1DussQgguTM//6Ma5sXaHXm9AkjaopU1CcbOgLEqUhCRJsBU5IS9KHGBE2FBqcUK7UafdbpM2U4gUkZTUoogojrHW4JwB76mlEUvHT7C4uMSdK5dYv32TtfW1QHZAMuwOWdveY3t3jxzIVm4zNXOUqc4CidZIKYiiCADn/L2d7kPcWe88zrsHHrv/uXJS10RQSAi/S6TwOBHKnu4RtoMxx1pRr8V02k265Yg4ifE4jPNkdsRBvx8UAwAlFKIBIo6rSg4XKHZlTpkJIgyRUmT9A8b9AwqTIZVAFjmjtZvcfudNhr0uQhc04wSZlUxZxZ6wDPIxMq5TPzJHERU8/8RJxvtdkkHEtf6Yt2/fYTAaU1qPJ0i4eOGQlEgfIrJYaWIpcMbQTFKct/SLgkajRVTm/NyLn+BTn3uOvK6wzSUuPv9xTFJDRhrhHMrkeC2x0RTSZCQ6DvGVs7giQ0bR+yb+fTsJIXwYj0cYayt7CMaZVRkBJiktWbnIAiLviCMd8tLGoMscjMFZGOaewicMy5zd7pCsKKkhsKZgNB4zKWdLkyTsmPcLNnzQ8B6TDdhYWaXb7VGWJbYMO7B1HmMsRZbT7/fp7h/Q7/Xw3hMlmqIssdaQZfnhAvRfPfuxD/yYhxrnwswUZV4idZDfMEVJMR4yGHZBCqZEm1hFQQjLG6QOOb3SGIQVCBsx6ncZDg4ovcPmBYmUiFhjhCNKNFGksKYkjRvMTnc4cuoMs/PzrN65y3e+9xrrWzv0u/tkoxHDUY6VEUI6trf3mV3co93pEKcJoiIhSBlWMS88tgJupAjsEIFgsn1NyNkBtJrssiLIg9y3y953RwK6KcShbMUkylDSo4UniTTtdpPR4ADpBNaEAgAhwuo6HA5QQhDrKDxGC11roWJJpEPlDRKsKRl2D3BFQdSZpxZFyGGGszc5GKyhxR6d6ZSp+VM02i1qrWkQJbW5RU4+EXF3bY1Rd4t0pkU58oz2djjRMLSbKY+eep4vv3KZG5t7eByx9IeqD7UkplVvIJwjH45oN5rMznZY3dkk1VOgaly8cIxPfPoZpqamGCZT1M88Tq05h5MK6R0ei1QSqGQ0hQKX4F3AHaR1IEucmgRDk0R1wGmtcxjvGJmC0lpABqJK6ShwqFgfekqSykCdx1rwzhDhEWURXNrSY50iSVPqjTrs91FKkg2HyBJcXuIyh/cK4wVJs4kTDonBu4rSKcRhHnZCXnDOko9G3L18mZe/9zJja7BWIKzA+pArVhUhRyACm81aMjsmy7IAqjoRXF7/4avAQ41TFwVHpzuIqMUgKxiO+kQR1GoSUxo0BZGA0hlckeMNoATSlTirGBYHYZILgcGhpMaoiF53jEhiau0WduQZj8fYcod4ZY3lhTnmp9osnjrLZ6bn+JP/8Kfcub5GMXZI3Qadouhgi4gsc+TGMDYFKIF1YRUVEqQInFkpBFK4SYh8uEoiZHBd4fDiQzBcPzHayp0NK7esSr6q9MR9u6Z1gdxurMfJEHdLQEa1sGFQYrwlLw3d4RClgyJgrdYk0RFpmhBFGjFBhq0l8zlpp8Prb77ClTe+y6994jnszk2sK5k/e4p6o4mME+I0RdUakJYMNte5tbfJybPHaTZTirxgdWWVY3MdknlNb+y4fZBx4egsot5ga7dL3XlaUjAsh7Q6U2TZmEE+Ynamzdz8HINhxjj3TDVjmpHnt37xs8wudjC1DlH7KIvHTwaVQOuqGNIH0KeqUkErhBdIdCVBagNoEjRHJ5nO6r6EONgaw/7BAXv9PgaB1BFxlBJpRaJk0DsmpO6CYRrGJiOyhpo3pK6qPvZBZK3WaDG3MM8jjTbHluYZ9A4oNfT6ffIsFPY7WyKlpyxzYhlYYAL5AHgsfEjP2MJRDC2X373B7Zvr5FIgREQqI6zPcVjSJCHSmjiKsNYG705HqIZGCElRlJjS8LAt+qHG2dvbZ25ukbSuSTXEIqawBWsra2yurtFqtpBSUJaGcTau3MUADCnEIdPCOEtuDEpCs9lAN+pYrUgaLRwSrSNEHJOkKYN+l9W0xtkTx5mZnuY3/sFv8pW5l3njrWtEtSmSZoeo1aa1MIdqzVA4SV4EpFbJcMMCRG1xXhAphVKSSAVC/GGC2E9yUdWFPwS8J6mZClGrcnJuAt8fVhGFndZahzGWQWbY7Y5Y29xjPMiYqscIF+D9SCU4UzLOcoo8I1IZ9bSJk1FAwGW4BofumnO0ZlOGmcVazcL0NOP9O5jRDq3ZJdLpeXQSoyJNURSUQkFkOPXkC5w8/xiD/W289wz395ifW2C+Pc1gv0d3tM+x2YjfPDZHdzzm7atj9od1VjfGpJFnPOxT5iWtZhOVxNzd2mE4LmhPzTLqd/nlz/00T144RaE1I93gyPHTIAR2gkZae5gLnoCGcgKjEu4RMoQDQfYzoKqH98OHmFd5qOmIREeUpiTH4RVoJSstp4pFVoUm1jusdGALYmNDmFAlvaI4Iqq1mJ3z6PoYX+ZITSihy8aMxyMiCRZHORpgigwdpYeSo4cr8GTR9uBL6B+MuX5tjc3dEYUMmEzsRCibVFCrOeIoIokMkQ5zUGuJ1hq8QKsIraLAbf5xjPMb33uVC+fO8/ijj1GLYmQjpbCaVlrjvd1ddnd30GkVRLtQyOocQczKhdXNew9KkpkSbw35KOPE2TaduXnyImdvd59ub4BKJGmzTq3eYuAkg91dLpw+zdLCPD/1mY9y4pFH2R+CUQ36hcUBowI2twf0B4ZIa7QSCG9x1qKVQgio1VIatZQ0UUSxquJ8UXm0sgIVqlIqOHSXAgoL1lYEhKB1AV6g5ATM8FhryfOCnf0R126tcfP6bWbqCfVI0YwTvLVEOqFdU2Q2xOWNeoN63EBLHfR3nAtkBKVxQmCsRSiHloYLF5/koKlZX3mZuUaD9twpVDqN0h5TFkQyCYuAMOh6hNMx9TjBFQUqbVGOR+TDEXaco5opIwMrexl3dwV3D2K8zSEukECZQ2tmBisku70RB8MR9UaTg8GQ4/PT/Pwv/Bxj2eC9a3eZXm7hN7Y4O9UOhIMqXhUVyoq1gbzvKjkSV+X1KhT8UK3PV79PwDUPGsl0vYVWEfvjMQdFHhZ+QhorCIyFBVRKEd7WSqTTaA/KBxV5JyWypmmgsTLCsMuo30WrFkoryqKkLE2oPcbRiBWJrlJz97PHqv99WJtRgCkMhfHkBgbOIIUhNiEvLCNFYSGJDXGkSJOIKNJEpQJfVvzwoDWs9YdTBB9qnL/7H/6YF557HqFiHjt/jjSKiKTiyPwSy8snePfqFc6eOMXJ06eCq+EFeT5iMO5ijUUEdUuMN2zt7vD1b73B3bVdPmk+wSdmO3Rm2tQaCeurq5TDA7KdA/pe4YjpJTXy/X1WO21Onj7NyYUFpoqEle2c3jhj2Ouzvr9P7kqU1pVxOSIBGo/WQfZw+fgyR44sMj09RaOZEke6Wq2rFMl97BIBWDeh6okqVSQPd8qggucrwMDjHOR5we5ej3fevsGrr71JogSdaAZlHfUoAZVQi3TgGssGToCONfW0gRZR5V5DaSxaKArjGGUlSroQC0pFc/44ptxFxyVxaw5XWsqshy0KhDWhpYR3lE5hkVipIRKohiZptBBJl6V2i+NnJdpJ7txYYau5jT19hKKWsuccf/iV7zEc5gxHGb1RRm4sSaPJ/mDAU49e5KlHL/Lf/D/+B+r1Gk997GP81qcvMD83SxSH1IMztjKaYByHvBdXaS85hzUBSBQqlOL5yhXxTGqFwCuBllCPg7ogXlKUlnFhcErhlEfIB9NfAgleoVAoIdBSoOIYGengNOuClpL0hj36/QPSNEInceXthVjbGMuRhblQ31pp9x6+f5UDnuS9EaGIX0aawriKXWXIrQWvsMZUZZARxlqM86isRCPxLhikVmGBieIPZyE91DinmzVWVjd49a3LTDfbnFyaIY4Tptotzp45ye7BLp2pNo899jjNZivkQG1JlmeUpUVISRQphHAcHGwzsIpX//Xv8fVvvMT01BSf+dQnSNsRyaDBtbs3GAwGCCJiHdOIa+xvbaM8rKzc4fFnXySeWiZWjpXr73H32m2G3T5xq0F7ZobGVJu0FpMmEQ3hKcd9tlc2sf0u5AXChxiz1azEgqW7lxs9TJBxzx2r4iZZ1RtKKZFeoESQVHTeUxrLcFhwcJCxcXMNu7/P4uIUR/2I40IwhcXHnsIXlL6G8ylOeabnOywuHKGetiEmLAwOvJHs7w9RacrQOHrdA5Iyo6kl03MXKXub9A4OENleOCYbuKNBKEtgvAftA3lAVAwmB3Eaobxk3OvyvVffZnuzSz4ag9S8emeTd7e7FM0OYwH9Xh/jQMaKbtbjV37lF/j7v/KLnD77GG6/wAx6HHvuAq3OLC1dx01iR31fvlsQQDMvAlHAW7w3+ArtFipUqPhJYtoLpA1urvMO58E6g3VBmcGJ0P4hnng3lSkHPEHipMMqj3WhzFEqXRXZR4gqZ61IyZ1lfWOTKIqp40m0Zq7dIjMl/aFhqtM59KhCxksc4hTOBxUHr8ICEtVr1Dst0jRGlAXGgpUe70L+0xlJnguM0RSFQQpQ0od2JSoAgFoD4sNN8KHG2Wk2OBgNefl7r9JKY9IXn+bosaN0ZmZQcYwVgqu3brKxscL5CxcJuscxKlZgK7K1FKhIUK/X+MIXvsD1W3f5yhf/hpe+812Wjx3j4qPnOH5smW7W49V3Xmd3dwtbehqqQY2Emky5ubXFrZ0dnn/madrNGabEJu/sXSepz7G4NMPCkWNMz81Sq9dItcSNBhTDGO1ydnd3WBWKVrNNohTKedJaXNV8VlxeqnrSCqcIK/qhKk74X4TqeESY+NZ78tywv9djbX2L/rDHnPI8KjOe1AUds40qFF4n9LyiXzaRUYekNcuxI0dotKZQIkEqU6UOHMOs5L3rd0iaDYoix+c9phoN8ihFiQhRCHp3bzIXj1GRCimZ0JMBpySFlaAFKo6QKg6pLRuQx/2DAdt31shy6BvNys6IkbPcHBasjj30DkiiBN/UODzLi3P83Gd+itQ73PpdxjOznFw6x0LnDGI+xWoZuKuuKgSoChIOUW9VXUhPMFLrkTo+rI1FqlDTW/WygQmrx+I9ZEVBbzxidzxibzxC1mqkgJYBz5hIn9xfGuqcw6twLVy18EoCwws8ZVGwtrZGLU05khxBa83cwjy5DLI4ygXNYhGXIKLvQ1KFEAglw+6eaFpTLZI0xnhHYcpQqUQIjZxzWOtwrgySL0pirSdSAiEhmhR9fwDB/ocyzlYtoj+2DMYZL7/2FrESfCJOOXHqNLUUTp8+w9hlvP7Gd2k0E5aXT1cyIlWMZoIYmC08shZx6tQJ/tHv/Card1d5693LLB39Ds12gxPHj/L442eJpi1Xbt/g9uoq3f09dvYNbgjORNxcu8P+9gofffojnFpagBcucu3uLooMXw6wWYrxlkwLTDaizMYoFdS7rRcc9EekUxarLbkvkDLo3WR5QVkayiJMYqmC/GYwxqp0qAKSpBCUzpJZGBUl+wddDrYP2NvZpTsynFCSem+NTmtMpHtYb1GmQVO2wc9RizXON4kkeCkpPdjCMBqXjHPY646YXlhifX2V2ZkpCpvRH4w4IKOW1mmpOt1S09vcYqapSaOEWpqi4wjjPEUeSAVpLUXFdZyXRDpGJilzCwvMzbU47zzGRhSDMQrNF7/+Ct98+yblVAsnBWdOnKRmLG9/7W85sbXBExfO0lya5eRHHiVpLCLHgTgSAE1XLcAK4aPgroqQ9/Y+VJ4Eu5CBpuYUk34lVCrvgZfvQVicK/EmpBdGoxFbuzvsjoYUOpBXlK/CClyI2wiugayICAZHZh2JUghn0FbhERhjyPIcVUmyKKVIkoRhXlD6sDDXdEQ5GoLJEa4An4S4+L7FGaqcKw4VC2qNGjqJ8VlZlUyCtya4+D7gEQBxHOOcqsC/gMo7JEqIIKnz4xjnVLNGNxOUecQwM7z6xjuktRpCp7RbbUpjmJubZmt3lXfefZN2u8P09FyFunmKUYkpLSJRQcojEly8eJZf+dVfYnXt/8Mrr77JqRPHme20aXVqnF48T+ElYwHEe/T8gMG4T2+vh80ytra2WNs84FMf/ySL80dRUnLt1jYuzxnvHSCVDl2hpCPLR0G2MamjvWRzMCbf7dG2gloaobzBlIbRKOPgYMB4mJEkNTqdGlNtRRyHdIdSKpTEAcY4igJ6eclOd8D6+iabq6uM9g9g1Ge6WMXP7ZGNNqHeR3lQ/ZTSLNJoPUbplhjkQw4O9mmlbcaFIS8KjA1VOgfDMYkSTDVSegd7tKc6FINdlC8YZwUyipm+8Dw3XtplsLHKdKtJPSloNlK8tJQYvM0pR4pGe5ak1kJaESRDrAnkcOtwxhEriyjGfOb5R3jqqcdxzRprt+9w+833aDk4dv44C3WPGW1h8iO4WFIkmtR6Ihn64zhjUd4GqSAqgjuqqnIJPOfDVLFQAel2hw0RgrtoQ5lXpUcYFPatJ1KaTquNilMyaxEEsOf+3VJyGI0cGk5mDdJZfJySRsG4nAuMnSRJWF5eZmFhgWazydj22Ot2yYucWGkiPDYfI9I6Imp8qF2IKiRN60kAjmSIm40D6e7DJyqDDpVKNuy63hNZifMuuLbqxwSEThw7ysisk+2OQQj6OXz95dcovOeZJx8nbcYoFXFk6TjXrl3j0ltv8/QzT5PWGyBiHArjIfYRLrdoL2ilTX7mZz7J7bu3+Pf/7s/5xte+SyuJ+NhHX6TVmGamvoQ0q4wyB/UatQXNqJ/TPSgYOsUb79wmzySf/sQLnDh6FH98ic2dAYPBCB3XiWIZpC69YmAKjMmJxBiVlfhej8wYaklELdI4L+j2xowzQ5IkzM52mJmuUa9LoihCIANLxVdpGGuII0eGINJJaHhUOoa9A/L9uyybdZIpQ57tILp90lqNPRmzq5foDlNyN2SmXUOIlKw/wjhNWQpajTo3btyh1mrz1a9+lc989Blq9RiVRDSSWVxZUBhLNs7YM7DwzCd490t/Qtbfpd2C8SilmSQUscC4kvFgRGPoOLIoSLTECElJDKVHI9EeTBnEv4kknbom620wr8bUTy0ickmr2aY922IrH+KnZtFeI607FDHT3gYetZswrsqglA94FUCSCZk+ZFcCEfwQvRUu1JBW5BBvQu8VIo2IFe1aSt13mC0txgREPLc2ZB+FqgoR/CRkDZ3dPEjjsd5iMFhpDvPZSiUkccqJ4ydYXFwkrsWIvQOEsUglqCcRzTgJLUF0hJ6Iir/fMKt0lxKSJE5DzI1ASY1XAdjDCawtK2UcX5U+WpSSFJ4K8ZfEWpH8uIDQwsIi6IhRfo2D4Rgf1eiNct6+9A7zUw0uPvYINV1jZnqO6c4+125cY3qmw7lzF1A6Ia6FFd3ZAllYLBpd18zNdPjN3/hV3nvrOu+9fYm3jkxz6sQJTpw6wVKjw/HpBdbu3mU/G+EkdJamKMuS/u6YUV5w7eYttPLEn/00R048SmNRstsrEKpOnKRkWchXiSTHCo2uNdBRTKTjINcBpNIjlSKdaSJFk2Ya00w1caJDtzDnKUpD6cBYS2ks1mXkZcHB0LDb67Lf36M32mc47jEeKm57uD0Ys7yjGO/OkD/9M7wlllg4+yzXLt1g8+4IvXOZpdURx08s0+lMMzM7TznqY71nc1DQp8FXvv0aP/2Jj7C5scXi/CIySkkTj45GWJMx6hmOPvkZbn7zi6Hqwhh8qhD1GKEiGlNH+farb3HiWJ9nLi6jhcEgMSLFu1AB4wYFu2s7dDd7pKpGGgmGsYbFszz7Mz9HMjPNjVtXmIkF50+eJsFDMQjawqhAxXO+InJMCtMFzhksrlrQ1H3zOxDdMQEgsjhEoE8FfqwTCK+DCFsUBURTKtKA0uGMIy9KxkVOaS2Ft/eYXIAWIW8e10B7X7GVAqnBe4dU0GzVaTVi2lMNnHCYPDB2bCRoNRo0ayk+jnBaEzR07xnn/fXATiY4meLQAXBTmigVjMfh3HOTY6xBqntNnaxzmKISL3OSSApiZYmye2T+H8k4R6OMNIk5fmSRwdVNXFmQ1CN2d/e5euUGR48dp9FuEsuIEydPkJU533jpW9TTOvOzR/BWYUso8wwhPTXVIBYRadTg7MnT/PqvfYH/+51bvPnuZc6fPU+nNUVrusFHjp9jd2WDtw+uMSoLVF0zd3wW9AH97QG9bMyV26sk33uT52izMH+MeiQZjQeMxgOsM3jrSKyCNEElNZSSoZbSO8rcMLSQxDFSBUQ51kG13DqLGZf0ByP2ukMOukMK6wJTZtynKIOL2e8P6O/t0d/dIu/vU5a73LUDXroVIVaOc/L5JxDzzzN36hTrK+usHmwzPT2HKQte/dZ/ZOW9aX75F36eK9ffJm7P4ptLvH3tJrlIOH/6OAeDgu2DjPduvM5zzzyDL0vqcUykJe12wl43Y+rZT9J7+eu0pCETA+qyjVER/+4v/4bvvH2Tubkp/ne/9nkePdYhJqfdjEAKCmnQ7YR5tUw53GRn7YClxy7w2Gd/Crcwj1hYwBYlZ88/js/7yGxIPh7idULabAMaS7Vz4VGoUK9pNSIKvU+914GYJ+/FaqHPqIZJZU/FmZVC4n2o3PAi6EN5KbF4LJVuVBxTjyLSeo28yBmMR1gXtKdSHRHrhERrFKEps7MGU5Y4Y0IOOHLU6ppYxQhpyLMRO/s7rG5vkgmYXlwkSusIpQ/TKB9csRQWmlgqGtqzPJ3SiFskkaYocw76Q27eucvu/hjjZKhFFgrrPV7JytMwFYLsWZyf/fGMc6rT4fadG0w1Eo7OT7OxO8aYhLwsee/2XeYvX+ORi+dRkSDyksXZOW5du8Y3vvY1nn/2OdqNDt5LxqMMrSJUIqnTQPsEFUs+8Ymnef2tj/Lnf/ENvvm91+nMdngyvUArqnHmyAku37yFrNpaRklEe7oB1nGw2aU76PH2O+/ijef5p1/g6OIywowoSkucNlBJHFb2RKKaoGsWX+wx2tlnfzyi8CEGUUqSNprMLh5FRjWccWS9PlvbW9y8eZvtrR3G4ywQlfNxUAzHh53DlghncaZAyAKbe67ncOLTzzDzuY9xe/c2S/mYT3/2l/nZz/5SVSqmKAZDujsb/OX//KfcWl1hcXmZ+uIyPo85vniOPPf85Te/zcVHTtGeXuDf/+WXWT52jFYaszDVYqYdsXTuFK5Y5sb6Kr2tdxF7B4zqnq+8u8IffecyfaNYMzlffHeb8x//efb3dtEpyPE+QhmGtqRMBOKxE9zs77GyepknFn+B2tH50AS4tAiTIxUBpDEFvhgyHuyiogQVp4ckce8ExgexMhHHeB1X3c2TwwoP7yvZGR/cWqVCL1NXAUTWWmxegJShRheCyHcFkU+IABJPmkQoWccYQ1HkxDoi0UHR0TiDlcHNVM5hyiJULKGpt1ooHKNRnxs3b/DWu++wsr2NiTQfOXGSpNFA6hCOTaqS3j9CDWaOZsDjZxc5OfUppptNhJLkxtIfZaytbXDp8jW2dvYpTECphRRYkYH3aBURR5pjRxd55NGzP55xLiwdQUWCG9ev00g1kQrKbjqtsz0Y8d6Nm9RqKceOzGNdQStJOXXsOG++/iateoMnH3+cVnMKUo1Sodt1aVwlSyKYnZ3i7/3cz/DWpVUu3bzBkXfnWViYY2F+ms70NI2pKTa2NtBC4nODNYZGPUF2mgwOhgyHB1y6eYmorkjqkgsXLjLVmUcnDaIoRtqSQkKpJMYZykGPjeEaV2+/xX5vO5BSpELEDVqLx4lacxinGPXG7O1s0d3bJRsMMFmG95bClqACf9eYEucsSEkpLFiHpuTjv/7TnHjiNL4c86uf/BUaS0dpxQLpPU6Ewts4lbRnWvzOP/3HfOeVt/iD3/8feVpb2vE02Zbi5pYlKyV/8Wd/yRd+/Zf5xCc/xXvXbjEeGvrjHlt9QSwKnr3wDMvPfIrbX1wh3e2yvdPjO69fDdA94PIhN2/c4uZ6j7NPfJzmwiz59gqbV15HmyEbm5vc2lljbZwT+Zy9XpelmUWkjxh3g7eTNFJwoVWe8gWuzDDZmGIU0goqUiilkURgEmypcSrGJqZqaBxSEr4iIvggQQg+yIiqqvpFiNDOIxuNEYMBtakOcbOJ0JN62kpaE4cSEqFDlVOsFMK5QBWtdlkP4Dwq0qSijnMG6T3aWbzNsRYODgb0hhleSqRWTM/NotN66MPDPT7t9w9BiYR6g2MXLuCWl9FS4aTCqQhPxjkMn/Y/hxQKrTRKRSgpQdhKHbTqTldpZf1YxvnOndtcOHmGMy5iPHyTqaZno+cosxTrFNevrzJTr9FpxDRbDUqv6EwtIEXES995halmg8fOnmJ2eo4yncbKBOMkVoKQMTqZ4dFHn+IXv/BZ/tv/1/+bd996l4vLp2nUIlqthIuPnGV9MKDbHQY3RVh05IlbmoZLGOwP2dvd5+133maq2eDsuRMcOz5Da3oxFH7j8FKhtQ4CzvmQ7akIP97h2pUueVGSlYZB74D9fk4pV/BCkWdjsvEo9IIxDmcF3oTYyFtbqQmEOshsPEJIRawsv/lbf5/pdsyLTzzD8YVFPBpX9CjGBUJoUDGIsCtIK3BlwdOPn2P6v/wv+d3/77/mp154nrXdFRZnL/BXl+5ghjFvvfYun5mdod1OqXem2d7cZW7mCJghL1+5QjsRnP3l3+HGl/8E//Yb/PZHH+ePXnmPW4OSHLi7vsZf/PVf8i+eeJbb+yVlFjFz7iO88rW/5uaNFfJ+j2ZU5RuNJUaFZr1SUAwHNNIpUE08FusKRFQnUjbEuc6ENofW4ZQBBd5WhdLFCOdLtIurDt2EKg0pQi8cH1WdtAXSBfpfIh2Z6XGwN2BjbZ10apZWZ5pGu0YUS5SOkFKGcsNK30lKWRl/RUqoqlyC5lWEmpA8Jq0li4jIQa3ZYexCKWDLFrTbMbbZQqkmSqhD8smhSR7WCEOkgiKlj1L8VMXjFaIiL5Tck/18sGQxpE1FxVacMNA+3P4eapx/+Ed/xed/6rM8evoEyydPkXGT7eEe1ufESUp3f5933rvMwtw0F6emMFlBLGOm27NcvnadV195g6lUEdc0RBFSC+I4QSkwziK8YLozzd/7mc/xxuuv8dLXX+L1199icX6KxRMznFs4wvB8xiuXL9HLHUQek+cooUlVC2Nh2M3Z3dnjpZdeYmF6hjMnT7O4uIBOgownUqKURvgYVyiUX2D5xDKba3eJxhk6cqAs2TBn2B8xzjK8LQ9FxbxXKKWI0jrtWgOhFOPRCC0ltSQijSOKfMyx5To/9bGnOXlkmhNzLcR4jzJ3eBtU4pWOsVJV7p3B5iNMMcYLz1La4B/95m/z+3/wBzz70U+xtXKVo7OeZ3/9H9HdWOErf/El/sE/+A02dneZObHEtetX+MRzH8GVs9y4c4uv39jkmc//FuPmcaK1y/yzE2f57vVV/uO3v8No7Ll2e50//KM/47M/+4sMuj1u9bdx9Wly41man0HZkrEpMdaGXSaKqLfrpMmErlYhl1LgbUDDhfaViHSJdyXOGYTJ8S5DMEZGCd7G+EJXuc9KoVZriBxSSSSVXEjVkkJ4E6qKhGfY79IdjOj1DpjqtGi3atQaLZI4RejK3Z10Jxf3ACkqaZPD1hIT/q4PAnJaQGQN9WaL0nqiKCFRJbW0hoxihAx1wRNRtw9CbMNn6+DSV3vsRJkBf79Jifu++Qcek/KD3/uHNs67K9v85Rf/ltHHXuCpJ09z8oxne1CwsjmEKAIdcXdjm7cuXeHosWMkSUIsJCePnmBjc5e9/QPurKxQb8V0Ik09jrBGYZWtDtMRac3i/AKf+/xP887bl7ly7QZnTy/TbEXMdOo8vnyMYXbApbs36QKl11gRXJamlHjZZ9QdsLu/z9/87d8yMzvLb8/NMDc/T5KkQctIScoykOVVnNBoTREldTwKYSyWAj0u8SanzIYIZ1FxipIR7XaHs2fOc/78RTqLs4GJM87wxjLVrDM/3SIbd6klOU8+dpqGMjDYgbxAFpVxeokVgXRgnMGWGaYYYYsMhWDkBc3WLNMz03z9W9/gmWdfRKWK9mzMzZs5b759nZ9+/gbPPHkB26wx7u2ws7FJb3cPpGd99Rbf/sqfcuetdzg5vcip5ZOcePQF/snjz/Ov/82/YTAsuHPlEsNnnqOQKS+9c5vnLh5jafkE5c4KveEIg2V3d4dTFyy4HFx5SJ0LhIwIISK80yHJ7oJotWRSRwvCBZV6b0qMyXG5CrGf1KF7t9Z4ESOURroMIRMCh8eDt+GeTk1Tm5pl6WSEQSFUILcrWXFonUc6glj4ZHofVqpUE/6BGsAQE4fPkCA1DglSUW+0mFYpLZmTpnWCstEPoVU7ISfICe/W39sC/eFRff/LqtTS4e98P+h0/3h4rxSl2Nnd42vf+g61ZsTZ88ucPX2and336BUZcb3O/v4O7169wZlTp3js/FnqsWau02T5yBLXb15hfWOTmZkmSXuGJG1SSo8xY5TWKB3jLSRxxLmzZzl3/jyvvvQar731Lu3ZhNPxMtPtGi+cO4UwIy6vb7JhBxih8EoT1yPmmhE7a5bB9oAba6v8wR//CZ35eX72Z3+W+anZasEUWBsYNIOBYZB5RNoKwtXeYIQjKyzDLGdclGghEQXEieKRM2f5+IvPcfb0CdJmilWKMjPgPEkqMaaPmG0yP7VIzRtUMcIXAd3ESowJgIdDYr3AOI8pMrwp8daSmRLtc7K8y0IzZrdf4+233ubU2Qu89sU/4/aBxNWb/Lv/8IdcOPXPGQ8EM60Wvf19ymGPf/s//muu372CMWNSB+Nccbc74sTOKmcunuenf/6XeO3V11i5eYt3v/t1PvGLv8XJx55h5liHaZ3z8s2r9Po5mBG3r9/gqSeeJEpSTF4ghEJH9dC8uHLbIEH4QE0T1qJMDl4RSjUF3qnQZGqyG5qqGkWBVxGIIGpGNTHdZGJLjUo1kQilVL5S6/eiosZXKZvDeiBxT1yNwyKF+3eqaq/y1e6PByGxDoRUlMYFZhMOJSOSpEYIAiesoA8zmsnnPfhZD1jcB43qYN4v/flju7Uy8hjnWd/Z4Utf/QZSf5qTx4+zfarPa5dvk9RnmJmdp7e3y2tvvk27lnLmxBGSVLK0OM36Ro31rQPmFgckzXXiJGWuVsOJ0PPRmgzpFHleUuY5U50maMn1O3dZuDpNe7bF/LzgyNw0v/y5z3J+ZY3/+K2XWO/1ac1MoSJFZHJkTVAaS76XcXdljd/93T+knrb5+IvPE8d1RpllnBu6vT5r66tcuXyVldWdoBNrDQe9HnuDEVkRkv0oifcFcaQZjbtk4z2KUZNETSOIKQdjbFFQxh4rxxw7tUg7kSg3xoxHmGyMtyZU5yPAW4z1FNZjbaXiJiUWF9o/FDmmKCiGPYQT/Nvf+5/4zV/5VUzcomSGmcUl1t97mX/zh3/I+QsX2O8VvH3pDV76+rdxXpKVKTOLp6gnKVOR4+iUZqoRc/PGVc48+XE+87mf491XX2bQ7zHcW+PsiXMU2ZhPfubnuHvtXXbffA1Z5uxubTDqdZnqaDAOHcdByaBqJuur2eSReGED7U5pXFVsLmQaAA4HeIP0ZZANESoImMUpPk5CoyAVI6qic+cFaI8TBIRX6orWd59H6LnHLJq4kPeVdIXnfb9BVaKp1YY2iUsF43xMr9/lYJATNWVQ8cAhXNVqQwROMIdu6z3D+0D1AjE5RnsfZen9T3mwd8phHPsh9vdwJYQ4Au+wXrKxdcC3vv0K050W584c4drNu5hsRKPRIhuNuLO2zuXr15maSqnXY5qJ5OjSIpdvrrC+mzEzPWDzzi3qaYPW9BJWgRFjsqzP1voevd4+C4uzNFp1uvs9rl5b4dzpE0w1Emq1FiePHuPI4jHqKuEP/vqv6Q8GpLNT2EZEM57luIi4+/YtioOCq1ev8Pv/7vfBZZw5fZ5ed8w7l66z2x2yvr3D5s42RZ5hXdAxMmXBeJxRlgbpFR6FAXplxuWVW2Qu4+rtm7TSJsW4pN/tUuZj0prgV3/jF+k0aig/xGRDvC1xRUHQTXU46/FFyMdVdJJK11YGJosd462lnzlkVOPylTdxHl597TWe+NgnMGbEsZlFXHuBv/rGq/zll7+FzwpEI2J++TRK1dnY2iOJ29RbTU6ePMJUPWav2yNdkvQzaNcTfvN3fofRzgp5t8cjZxNu3Npme3uH8+fP0N24RCOuE6eeta3boCGuNfHK4jE4p0OlR6WtJKsaTC91aL1AGhzCStbjkI8aZiBUihQc9q4JagCTBrXy0NgIi8EDE9ZXbmv1qL/vfe+3i4e4h4GUE3YtaRzSWkajEb1hn7J0yLhBlCqENwhnQiNlISpKYbC6yS58n2nd99Mk1q28hA88FsGkYdbhsVbEhg/jCD3UOGMVESeC3JZYU3L95i1ee+NtPvaxj3Ph/Dm+9b03mJlfJKk3GOys8971axxZmuHs6WUajRrLS4ts7B5w9eo1Ti/Povt9Vu7c5JiQRHXNqDhgfWOLfq9kaX6RZ558ile+9za9/pCVtXXefe8a7WYNazy+uMO5sxf4/Mc+y+d/+gus7O3yR1/8S757/bXAtUwV00dn2TU7DHp9Xnn9ZZS2/PIv/hLNepMbN9/lncvXGYzLUJJU9QXx3mGsqZTlQ5K8NICCsvSsbO2xub3PG2++R+QseVmQK48VhuX5Kf7PT/0fifIBxWgPmw8RRYFwDmcCAdpYU5G1K+Suui+uSqBLYJSNGA5zssJw5vxjrA8Fg/4BJ44vcvutW3Sdojk/z7y09A72QwNYBVFths70Iq2ZU+zsbJPngsK3eOPKXVrt0FgokXVW9/YpvOPx5SO8d/kGRRRTr8eMR5ZIRextdLHTioPBPnfX7/CFX/pVHvvIs9g4zBCpNF7HVefvMElV5dqZCTDDfcBRNXEP+1sKKpfY37cPyfvm+qSqNpDYRSV6fc8E7k3myfDcU1G831jvDzcnuVF8aIYlbNjV88zQ7Q3xQoX772BcBFReqaDeB/fp+3h43w+Hx3z4KcHqEN5Wm7p4oHVIuA6Vy1zlfA/dY/3BZvjwXimFQ0lFkmrKwpMXltffusz03BLtdod6LSYvctJajVEcs7Pf5cbKKnPzU8w1awgUy0eWePu9K9y6tcrjj51mZ3uH0SgjqUm29te4dvMO584/zYvPX+TsqUe4dPkmN+7cYdAfc/3mHU4fO0YtjtgpdkniVRaXl2nV6sxPtfmZj3+SO1srbG3vUIxypjrTtHWb9eurjPsjvvfam8zNL/Czn/ssL77wEbZ3d9i/difEHjbDOX+okmo9SCVD2VAZWhB4Cd45MmsZE9TmCyHIhMX7nF945imkG8FoC5F38dm4SthbXGlw3uBdGTRMhcD54CKKqtRLShXKz4xh2N3l1e+8zYGe59d+7VdY21xna2eTp47Pc22rTzx3Gn8wYnquQeFynIdGY5rSKXQaMTMfs7+/h5cJ7dmjlM5i8oJB7hkNDY2WZLWbIacWef3aGkuLM8wtLLFxUPDapdvEuiBSUKslvPjRAfXWIj4OUipSRVVZWDA84e/FfWLSj9TzgHF6JkV399DMiYs6cY8Pf6m+wh5qK8pdZWbi3sT/vsa6D/w2adrr7zPUyYeFGld8UMYbZSW7+4OQblMCpGKvO2CcW6QMrSiFrPqIHmpFhZ+DuuO93w9lVieKjj643lLK0BfnPgMNelQuiE37oH6htPpQK3yocdbjFGMMUmump6cZDIb0uj1efvk1zp4/RaOestXtMzMzR709xWBvi1urq5w+uchM/QiR8hxZmGNzd5+7K+ucOnmMTqvFxp11BsNduqNddg76LB8pmapPgVa8+OIL/NmXvshgNGBra48bN1ZYmGvTimq8d+My37vyFo2ZDsQRw7Jk6cRpdjOLZ8ypMxdpqTpx/S0uvf4u/UHGN775HU4cPcZTTz3FZz71adY3/pStnR7IGkiFlwoZRygVZCqDvAqoJCGu11AyUPsaaY24VqfRatNupDR1wb/8J7+EGx3g8l2weegrYwj5OyEpjAFhCc1yg3yHNRapaiFf5xzjvMR7h82HLLYTdja26G2u8qnPfJp/9wf/lufPzHLFbDEwOfX6NMpnodwoqpPW2vT2++RZztzcNBbLyuodnn/h+Ur6Eq5ffY/xIKOejDix1MFj2V3r8dijz/D6q+9xJBEcX54jG2f0eweMRzmXr1zjF6IY4hREFBTShXhgVwozMlDQKmvDe3donMHY/OE+elh3SUCA7+1qVW+TygCphEiqvx7KyPwwXbuojNPf93PwikNsP/FgSusojMM6h9Ka0sEoKxmNSzQFkhIhfejYfhhnTt47EPWVup+5VB0ssmI9uQpVtgjhDpsiaWxVIRM0a733odds+sHn8lDjtFlOWk8RSjLdaTE11SLLhqxvrOJlQXt6jkQJ8vGItNZgVGuxutPl7fdu0IpTZpoN2jXNuZNHeOWtXS5fv8NnP/kpTk0foTQFu/tb5JcucfvWbdbWV1g4epQL58/yqY9/nD/f2We03+X6rbucPDFPvb0cAAip2dg8wCrQjcAVnTl6BLc/RE/Po0TC8tNPcWtvm/zabXZ2d/ij//DHLM7MceGRx/jn/+J/z9vvrSBVA12rkdbq1Jot4jQmjWPSNCLSgb0itUbpwBVVSiNVaEWhizHHZyTHOgYz2Ef4AmkNwgSFBGsJQtw+wjqF98FtFkKipcR7E+JdUyDNmNKDrreYbaa06bJ8dJ6XvvsWN66vc+ZIi+lmxN6oBC0ojaPVmKd0nt3tXaYaTfLxmO2NTZrtFrvb66SJxoxz8qJkefkEa7dvsbGyzfHZFqdOHWFz5WW2d89w/sxRhje+Rzu11OMI7+rkecmlq1cZj3ukUYrSSZWGcBNrOZwfoqqFnDiTh4bh7/39XjcAz/0z+dAl9ISO2w+kJCT3zLeKX8XE1P3hTjr5XFHFtYeusK8+ocp1Ck8oRfNBiUHXYj71qZ/iiac+EjqGU7K0uAC2xJZ52PGFxzEJfajiw3D+VaOR9yGt9/Kq4pCbW9lRQAaxflIscE8s+1Cl/Uc1zt5oQCdWpFHKeDBganqKpYUZNjY36PcHNFszRFqRjcfoWBPXEoajLivr29yZ36ZxKiXWktlOi5mZaVbXNtjZP+DY0ePU0gbHmh1Kp3nzrde5dfs6s4uzzLSbfPYTH+e1777ClYM+O90hKytbHD02T9pMmFmYZ/7IabrjATdXr4KwlPWEQW/AjWuXmW/Pg5Acu3iWsnvAeKPHyuomf/u3X+XoiaM8+8xTPPL4Uxx0R6g4RUYxURyTRpqkar1eWovzofyntB5H1cDJA85RSwTTbY3iAHwR+j6WVf9Hdy8e8l7gLXgvoaKweeFwPhix8JZIK5r1iF6iaMwe5fHZp7i1scdfffmbmNGAq9eucvHi8xzcHVJrd1BRk0ZzBi+gntbZXlnDFAVZllOWlnZ7jm9+6xWWji7RHfSYbrc4ffYMg4N9rl1fp15rUZaezZ0DHrl4nqi+QF7WECIj0gllKdje2uHunbucfXQW7y1RdJ+xuMm5VftopTdL5abdG+HvQbh+omRYTWMxUaudpDgmaQnJ+/OE308+v/fukwVBQCjur4Z7IDYN8WbYpB1OeJJawqMXL+As9A9GrK/fpZXWibRE6sDDdZV7LQ89hvssceKfVymcewsU98gP8OBOLyZ9ZzjMpXofOiF82HiocQ6sRYzGJGlKs1ajlabE83MU4yH7/RG9/pCk0WJc5Mhck0QRWml29/tcv7XCTLvJ/GwHITWLc3Ps7+xx/do1ZqY6lMbSHw7p9gaMCsvVK9c5urRIrdni6Pw058+e5PbdFfqjnKs3Vjh75hgLYor2bMQTp84gkpjpRsyllSto6akvL7C+ssP2yg18UkMqSaMzzXAnw1nLu5ff4+Xvfotjy8eYnT+B9YZulpNENajkM3ElJi8oS8donDHOMoajMUVpKUrDMBvhrGUm8Tx75gXEaITIM1xeYktXNQKu5DZ8cPOkrMAPY0MzIB3qIb1zGOvxWmOHfZTWrBZN/uP3rrOxv4ko+yQqYrBTYE5kSAq0biMjTVrXCC1ZWjxHEqXcvHqNWl0z3W6R1Bv0Bn16vT71WsTt29e4aSzNRoMTizMsHZvnGfs4m5ub3Lq9zjNnHmPu5ONsr71LkoQOXKXJuPTOW5x79Bm8DBUV9wxmgqxWxidVZTD374zV0w6RW88DbDgpAonBhT4ih7Kkh43CJiDLg3HmxOje3+tGQODbHiK6DwJHpqLTHfYscY5ER9iqO4CxZZDTSTQyUYCu6jlVEMi+z6Xl8Ep8cDeB+wutH+j/CodSnpPnTTq1f9h4qHFmQuDHGdH+Ac1I066lxFpwYvko0c4OK9vrpGWBI2IwyFmca1NP2vT2d7i7vsWpk0c5duIY3nnmZ2fZX5jnzq2bPH7xPAvzC+wf7HP1xg2u3F5h72CA84JPfvJj1CLJpz7+PHfv3uXS5ZtsHfS5s7ZNZ6pBd3cHURRMT0/z0WdeoD3V4Uvf/BrLjSl+7pc+wd3b6/zen/05Q1tSSxX1Vp1xOWC72+Wr3/waR48f5/kXE/B1rrx3g4NcUG9O0a7HqDKjzEb0ez3GowG2zBj1urgyRwgw3lLmOf/8n/w2KX2wAzAhlzfRX7XWYowJLlQlViXEvS5SzrkgfSklWgv6gx4DUnbjJnq2zoUnl7goc1bvXsYV4MeGxelZpo8I5s48x/Z+xuLSPM4KdveGXBl2kUqQjYZASqMRsXT0JMPeDqeOL1FeWGbl7jpCxcxPN8jyMadPHOOLX/kyZ86eZsgCFz/6Ubb+4gbSOCJjSaKEo0cWQamKgBBExCbG9uCclB+ap/swBQ6Pq5hboUgaE9JOTGQN7t9wxH2gDPfi0PcbhuN+w3jw85SsjtJ7bC7wxuKNRSJIYs3MTIfZ+RkarQay6iv78DO4fzz4eVI8eAL3H4p+f7/PHxBDPzzmFILSewbjjFF/iOtMkUQRtakG03NtRqZgbX+PuD5NZBXOKFr1GUaDIYMsY2N7F+tDpb8pHAvzc+zv77Jy9w7Hjx5lcX6OsrTcXdng7t11Wq0mJ06dYHF+ilhY5qabxBoGw5yrt+6wfGyWZq3GO2+/xXnvmFla4MTiGZ57ZMQrr32X5Ljl2bMXGH5mwH/82lfZKkfUUk0ZKXJbsHvQ42++8Q0WZo+xNLtMi5K//fZ3GFtBEgvMcJ/CDPn13/o1PvHoi6TSYsZd+rurfPebX+fKtTUuXHicj1xcxI3XcGYI3iJ8QCyDqJMFJl3KQhWGcWVIR3iHKYJh4j22zNnZ2WCvvsyX3t7i9u0esgSrJdvdjPMnLzBe77K/d0A84+nvb7B5Z4eXvvIn1OIOrZljzLQb9Pe6WAnd/h69wS6nTx+nVdcIxjRijXAla6tb9HZbRGdPsTBb4+TxC9y+scEjFx5h+cITzL1ylO2b17GuJIliptpTTAS1eeD7+6fc+6ypGpOdxr/vcUHV05RgMFKG6gxJtUPdjxxVseNh1lS+H5+9N1zVQ+VQYvPw88L7SQRYh4xjTF6En6UkjjTz8/O0p9qoWAfmkQ+qfuJDlp3vQ40/1MjEB12aH3o81DgTa8jwDKRikOcM97qkus3Y5SRpyqNnzyBvr7E/MFhb0u8PmZmaptGYIhtK7qzscvXGCo9fPIVSJbOdOguLi1y7s8byyS2OLs5xZvkoL9Wb7O3tcP3mLd54+zLH5ppsrd8mwhPX6zDYY3Ozy607ByxOz7K+8g47vdvMHD/D8aNnuHjyFEvtGe6srtKZjvn4iy9gKfm93/sDlAx9JvORYGt7yMbGJm+99zrNj6YcPZZw8VSDv/3mGwzHI3b2bvN/+a//T/z2b/4KDaVRSjIuuxx0x7z03bu8e/kS/9t/9o+JzR4uH1LmCmcTcONQIRFpJEEB3juJ9arq6ZliyXEiJxEe4TRDm3NrZ5P/6UtvkZyv0ZOzDEZj5uM6WwcH5IOI9y6tIPyI/OUr/MY//A1ev3mXts/4L77wIv/9H73OKFJE6Qg9jpgqZyiSgAaOexDXYl5+7xLtVHPm2Al+4+c+w2A4YndY0Oh0+JVf/3v89Z99lbIw9JXkxc/+Mv/9m/8NZtzj3NJZTpx9CkGE8wVaxvyos0wQRMU/aKjJtJtY0fuEld9v0O///YPGwyZylRjDqQpQUuCKAuEkWnpUkiCTFC+igMSKiYlHH/huQjj8fbvqg0b8PnmTB+Bc+8Fn8yGX9qEs37iSksQ6BlnOZr9HPy846A25tbmNsYKTC0fp6AhlcrKiC5GlNdVGak2vP+T69Rvs73dJ6w2SWsLc/AweuHPnDtLDE49d5MlHL4CA9d19XnvnXd557wrd/X0aWnBycZZWvcZwNOLWygp7vR6lK8iKPpcuv8HLb3yLkdln8fgcFx47R1aMGWcjFhcXWD5zklI5ZCqQscDYktWVdVZX1rl+8xbOlDz95GNcOLVMMR5w/MRRXnz+WRo6JpEKgQKZsrmX8daVDR5/4hkunD5FOR4EFpCtoHpCrtLZqm/z/a6YEJXSn0TLCB1prLdkTvEXX32D2vKTDG0dGbdozc4wfWSGT33moyyfOkJrcZ6TFx7n8Sc/zfqNNT57cZaPnxWcnt7ns09NU7M7NKdiPv/3f5ELH3uWhZOLiIbARpZU5PzMRy7w5FxErXeF1776e9x+669pih6mPCBpwqc++zHe/O67TEcLNI8+yqe+8NtkVhNpT5bvgrJYoUPyYOIyfuA/vv/rA2Kv7/viwd8nSfkf6rXvj+s+7HHAB2Jv0B32oKxFmAJsgcAQCU91lkG1AIVjUkf6/i84ZAFNvu5L1j5w7twXN/t7QNr7vz5sPJy+JxUpFm8FQwxrgwFuA5JYM7IGVwpmO3M06yn7/R7Z2DEuOrQbHWSkMGPPxtYOt+6s0mxcqCoBajRbTTbWNhkPB8xOtXjh2cd49+olVta3ePfyVUR+lCfPHOX0cpO03mE8yLly8y7r27usbO4yP3uMhICsXb11i94w58XnPk4S1xkXBSubq2zsbvLcc09z/sx5bl+9zRsvv0lvr0t/MOLm7RXmFpY4cfwYJ44s8Ku/8HlW7t6gNVdjqtFEV9QtJwS5FVy+sc6dtQGf/+gZZhopdrCPKLKqr2ZQOw9QuancWlF5ghKkx4s8uEpEOJcxtgXbQ8V6X1Ob7ZC5FlnfEtVrqE6Kjz0nlqeZbjXp7vQYjB3dgwM+8kTBN77zbdTPfordsmRja4+UDGU0J+bmmap5Hjl+kZdfegU1fZp2cxlvYu4OVyAVLHU6XLtdIGYsYn8DN8o4dvoIuc0YI6nPnuJTn/919tdeYzDYo7FwAmM9sf5+V+7vOu7vkfoAiyY8+IGveVieM6RaP8C9BqwPlMOJsl+v12PYC7F6nKZoY4jrTaQM6S6Lr/rquMqoJp/t70sXTfJFE7R28nm2Qq59NQ0qJpH3Vc773vl7H3SsPmyLfHjbea1IfBoKkp1haAx2t+T8zCwX5+fZHg9Y39+ijCLKSCCMYdzr02526Mx02F7rs3/Q5+r1W3T3uywfn2FqZppWqwmRIdaS6VbKxXPH+MzHn+XP/+rLHOzssFmLePHxR3jhuWeI6x2eeuYm/7f/7l+zurHD9VubLM7NoPaG5Cri2toeX/rKW/zu7/41p0+cIK5F1NoJ58+f4Ykzx2k3pxCf0/zN+a/xF3/+19y6eZdrN+9y4uQpVu7eYbHT4fFzJ/kv/sk/5O0bbxPHoV+JFT7s0NmQK5cv05ma5olHLpIKj/KhXKo0FluaoEAuQs+OcAMrTm0lMiyUxbsEVwpMmZPZkq99523Wu4LmnqVe15jCMrM0T9KGtN2kXZZ85MmLvP3Gm+yOJbsrW/zNV76MmjrKmjhF4xic3t5kt3uVuh/iy5g3X3mF06cf4ZFHLuAixYFO4MhptE8xec7sI2dZfX2Nep6ydfUKFHtc/JlTbG6scumtt3n+8Y/y21/4Rf7b/+d/xUtvvMMvnXiMWDg+RIjuQSP4EY33fu/i4cyf+8YDRvDgE++PMx84LhyhADrIcI5GI968ep2XvvtdjLXMzczw9BNP8mxzmrpQCGUCsXBCyK/SJJPU6oP5Wr7v+IO6/fefZwAM76VcJsaptebDWAgPNU5lSpSMkFoHhMt7cmfpjkccsVMsTc9wZ3+PoSlpNOrk/Yxet8vM4lFmZ5fYXF0jL8dsbm0x7Pcxbsy5KGa63WB3uEG3t8/cUoeFqSaPnzvOtXPHefn199jc3mN1Y5/nRcrS8jF0u8UTTzzGysqXuXNnjelGgziJ6I9zugXsHZSMrWVt9z1iYVk+OsNCZ4Z8ZgaV1EnTmI+9+DxZVvIHv/vvGff6XL9+hReevkgtTZnpdHj2I0+xdGKWSIMjI5QX5Wxs3uTWtbt00hleePJxbDYGYzFZFpT8hMSjMS6vGgI7jCkBhXcOKRTeScoypA2U0PS6XVZ3BnRLTeI1zkuEcFhb4rxmcyOo3l++dZf2whG2NjPS9jzTNcn00VO8/sp1tlcPEKZBoz7DtZsrNJ5Z5JM/9xuY0CibzBTMTM/S73U5Pj1PORxRjHpE0YhOM6GzfJw///df49TRRzgwkmF3m//hW/8d2fh3+MV/8I/527/8fQ7W91lYXqhqO++56R863pdf5P7qjSr1csgSemCS3v8ecH8U974sZyA+HOZlDkmBFch0f8pngl058CUGhSkLeoMDVjfXeOW9y2TG06nXWVg4yqNZTpQkIQdZgVnu8Nju7Zb3zuve58gqjwscduN+/7US4rD4E0TVy26yc37IeKhxUlpsFDRCY6FCpbzwbI0HJLvbnEuPs1BrUPS6OCEQSUxWlORFwfTyItPTi+xtj+mNBkipuHkrJMHPnamTxIJXX32ZSBpmpqe4cOwIP/XR57m7scvm2j7vXFmh/vWXiV59g7ubm7z8vddAQH8w5PKNFabaUzgP6cIsj1w8R31piXI8ZvvWNYpsn0sr18nNGD8q8AbOPvoEufXUanVG3TE3rq+xun7AjTs7bO85tnd3KESfR59+GutjSgdjN+Ld6++xutFjKe1w+tQibn8luLHO4YsC5yUOFfSEbGB8SAiGKDXWBK1WJaEUlnHpuXRjm1fevYtdeIzCQy8fkyQ1RuMRx44sMSpK0kixNDfNQTcnSZrUFk6S9wZ4KZhuz/L8859DxW3Wt7sYYzl9fAFlh7xz+RqtmTlS41HCcrC9SaxSFuYWORjs0j3YZWGmydKJ4zz57HN8+atf5Lf/2b/k+LGf4sZr7/Kt73yNfnmaE3NHufnWZTpLJ4m8Aschs+UBc7nfVv37fvyQ3fRhO2VQi/+QHCKT3efezvNB6R03oe1JgfSA81gJpbWUZkw+DtUoJUGOJU5ivIDCW6STKKuqsOb951t5QlI/cNSTPq5SykA04IMXsYnrfSix6VxQcviQ8VDjLIRAGItSiul6jf7QUrqSwgo2uwMa8Q5HjyzQKGPGo4xIRRDFZMMhjUaT0+ceZXtvFefyqjxKcHdli2a9zfzUDGsbm1x5/XWeeeIijblFHj1zhqefeoKvHrzC7Y2t/x9p/xlk2Zbd+WG/bY67Nn1mefPqedO+e7obaAzQAwyIcaAbksPR0AUlMBjUB0kRUigkhr7pg0JBihFyQVHBIYPD0TBmiBmYgRkAjcE0Gmg0+r3Xz5t65Ssr/fXHbaMP+9zMm/mq6gHNE5FVmdeec+9ee631///XWtz+td/FRM0wHBeBblGbmsG0RCY1URLR72e8/KWXeOYLr9HOIkQ1ZXv7Fjc//pBPPthlPV1CWcnv/fM/4ZM7dzkYT/FRwiS3/N//y/+a/5f8u1SVBeH4K3/tL/KTP/uTSHK8qynzAe+//T6zYcHGi8tIN8PZCqwNg1kD6RbUQzZI2YSQ2LpESg3Ypsu6QQhJXVn2c/j1P36furVKtrRCp9dByhbeSypTUBlHbSCKY6ZTj/ExkQwF4R/tDOmfW+P27bcodJe1zSukvT6JqWi1I6pJhY4cMpJUDvbGI3S3g/CSCgNKceXaNTw13X6Pb/zEFzkY7fD2j/6Q1eVv46m5euUyP/z+D+jmQ6IvfYVXilnofu5PL8bjBfcYR3p6XZ5V+HwaBDkuB2se/jge8/i5i693BgiaH/PlLpqHyHkHhGZoUFWVaCXRXqCEY3W5R7udoeIILzVSBuOcz9I5e00CfeqGeQ8hRChnFQvXc0qXIeb/B9WR9GIhRvj08VTjLD1o7+jGEev9PokU7BwdUQEj4dg+PCJrRbSyCDlzlHVNpDOK6ZS6cqyfu0hnaZ3h0YzKGtpZB+fg0c4BvU6H9c0LjA7uc/DoEWtJh+XeKi+/8CzvvHeTu3f3UEmXVn+VNIqYTSaI6QwvQtlWVZUkqYbplHJvh54wrLU7qJaim4V2gx+9dY+7H74DU8f+wZDhbIQ1oZ2lwOFERe08XkrAcev2Rzy6/0POrxzhXcXOzYf86A++h90vePniDex0hKum+LqC2oFXzQfd7LAuAAJaCjwGW4dua8YGJHdWwh++fYebQ8vlz32B1fNXUVozmTjK0hCpjKzVY3/7kHbcYf9gwLgW4C1ZOyHdus60cPz0177Ib799F1RGp2vA1Yw6EsqcNI7otVphiJGX1Ah0pDgaHQbWIokpp1O0lvS7bX7qJ7/JP/veD7j78EE4l+mUl1/7En/0P/59lr69gqkr4rgdVE2naAEWVEDNraeURP6Upz1pkCWaOs45Yb/ASQrRjF2Wp5537GmE4qyxLx6fltgRwmBACI2WEe1Wq5m5AhJHK1VcvHiOJNY4LXFiXjUEZzUDJxd6MkOlOcmTX89wsY8LLI6vQJzti3D6eLoIQSm0c2AM9WRCIgRxFFM6R+08g3zGg+1tts6vk8WaIq/wwmJtxe7uDs+9+nlWt84zGDygMjVVlaOF52Dg2d7v8MqNK4x8wYO9fdobl4k7cO3SRW5cv8ij+9t4lyPqgqIsw2AiBLYpYi7LKX2fUIyPeHDzI4bbL7DR7ZDECaWElZWLdHo93rj9p8iJwBiPlQYpHUp5vCEM1hESKWIU8PG79/hv/+v/kcm//HXqasxv/Nb3uX9/D5d7vvzlV7H1BF/PwNbNGPt5GZENfXWsxbq6ASBC2GKdwBLhgd3BgN/87g+JV69hZYS0Fb3lJZQSlJVFK4MxlvWNLc6vrrB+bpn37+5APSNqdRhZzd72j7iyOsJPjtCmQDnLdDql3b5Cb6XL8HBMPhxQG4fzkjjKaKUxcavFzsE+02nOUtamqjwrWZ+rlzKu3Dtg5/CItdY6z73yefZ27/BL/8n/hi+/9HxotCwbUcWCUuf4lzN/iGOD/bT3m4sJvOB4CphAnA4FvWxakpxexOGxDaBydsWf2SROHT48TwiJRBHpCK010kEri1jqtlnutRFYhAizT3wjEln0nKcvZNE4T/Lf479O7REnty9m5Gc+ycceTweEIk09K7BSMSsqUJJUK0rjKLwgF4r9mSE5LOh0O0xlSU2BTjT3Ht7i5S99mSuXb/Dg1kdQD3B+Xq1nGRzuMp2sIlQL6yPyShH7iHMbq3z7Z/4yK6uXGI0Koig06SrKkrJ2lGWJqUtaiebC+Q10BO3lJVb1KstqjSTOiEVFttzhZ7/xM5xLtpgeFkxnFUWZMxztc7S/z3Q0w1TNlGQERT7GWsev//YbfHx/htaGg8MxurVGp+24sLWBqHeZ1+sxX6zONZPUQ/sO2VT5F2WO8wbbyNLGpeWP3rnFQa248uyLrK6fR7kazIQITe0s165dYG25xWh/yPpmn/XNPrsHY3Ye5rS6bVK/z8ODPQbTHufXWhTjKXHLYH1JkQ/oqy6z4YCD6YRz159nMJmS6oxRYZiWM2pvmOaGWEcU1oPSRLHhS198he9874ece36LnZ2HofP+xgXS9U1UqlAqPvF8n1pTj4trj7OzT9/XGJOe3+1P6kPDc0/qPpu7F15F8ynjPD6F5ns581aIOftokMLiSksxqsCBwtNJYrI4JnQXacbah9ga8UTXeXJtn4VRP14j9WR0efF4qnGuLS+zN3tIZSxaK6RwREqSekltoWgqFPYHM+K4DQic8MRZgrElSRxz/dqzfPDW67gyIonCLA0hQUWK/dGAC5sXeOn5l9ncuoRqdTBxRHvzGV75/DewtUchKcuSsjLU1jUhbYEWlixROG/Jy5q41kx3JxRJyG+9qbiyfI7Nn1glL2vGk4KDwyn37z/ik1u3GA6OsMZj8prZZIip8zAHpHY8vPOIleUu7bTP0eGI5bUeUZTgi1Aky3FRbajRDN3oQiGuMTZU00uNsw4lPc5ajmYVb3z8gJVzF9naWGFpuUe/22ZWF1gbY/eOGA8P6SQGW0053N/GuzGbvYSWWucon+KLIb3WMoOjgq984TXevjUmEgaZao72HnH/7beobIxIl5nZmkc7d/jJz3+ZCMlgBvce3ub88gq2KpG0MLbG2JIsi7l6+Qo3P/mEV156jbq2jI3BxhFaCqQ6DYCcPZ7ktRbH5i2uTXH865wPfvIaXPRPj31YY8mPqTYNfCNzQwjTzGxtmrA0lHJlaQst45BHfioD/OxOfOJTzMrCefjTpjnnSuePO363J1z/06eMnT+Pm84oJ1MsBmdDRXmmFIU1AQ6XgmlZcTicYCJBUVUsrSgcnod3P+H8+Qusr68zGXuEyXF1wawosUcDZpUh6a4zUwkTmZJPa8b7A7wMxazdrEUax/RbbZRQIDylKciLKUVdUFUleVVinEPUhv29fcq6Iko0SgoiqekmKVmsMUWOKaZ00hbXr1xjsrVBXQum44LRwR4vvXidf+9v/yJf+eKrrPTaOFdTeYlxitRXZLM9alPjrG2mW4ewVSsVRsbrCFc1cxld2MWFgKKsOJqU/ODDPd66s8f6tU1ENUPaGeOpodXrU1cKby1Z2iFSEtXOWF9a5oXnrvL+u+9Szg65cP4ch7uazuolVjoKUeVIe8TG6jO8+dZtXv7GV1HdDjdv7TMoHLUVTMqCD+7e5Yd/8iYXrt6gs9RGkbJ/OODcxipda7HFjFarxdbaMj/4wRusraxx9cplRuMJ+4Mjzm+sHIepf97js7jP/6nChoVs98z/J3+Khcd6F3jOwWiEbUIAoVMsKrSoachM6d0xpXL63c7+/RiA6zj8bnjRM/exYJDHfz5hd3r6OIblZTovvsBHH37A8PAQEYRQIAQtQEeQJDFLcZutlVVay33ibkKr3eLoaMZo5y52OsAUU2Kl0VEXEbeYTAdMZyWT6T5ZdouXXn6Ncxc3SNqdgHhp1bS/AGtqpCFMmrI1RZkwmkUMZzlmmjOdlWwfDKnrIcJLRuMxtSmI44is3SaJM+qqZjoZ4XxFvydJshp7UFJbQDmidsrauRVW1tdIs4w4Toh0igsunqiaUk+q0OldiOA1m12wNiacqwtgQJIkGFPhvAtDeWuDEym/+d0fYZJzVCZmuLNHK07ZG085t9kninqsdmM6aUQ7TZBxxmw65f133ubwYMD2zi43t/eQKHqdBKVh+8EDdh5tk6xdYG3tPI+2B1zeysjLARurF2n1lxHXX+LBRzf53CvPc+n6ZR5sP6AVhybZlRWU1mONY6nVYrlv2VjS/N5v/jL/q//1/5bDvV0O99bYXFkG/Xj0dPF48tCfhbX5BAT2s47jkLrhHT1NCdrx63mawtn5E8J/i+8hHHVVcTQYMi1mmDAXgbJ2zGYV/W4Y5xfm4IR0xZ05h2PRxOkLOGOATwaEThEzDQ0kpIT48R766cL3SHPx+Rv81Le+ydrKMsVsinLQ63WJ2ym6kxFnGf20TSdpIbTGykD6joYzPvzwLn/8/T8l8kVoBC1ihIrRqsDVNc7U7D64z4dvvcnnXnsBrSuc91QQBrDK0KFNe4kzNbauqaqa8Szn/Q9v870/fZPdwwOcC+MSqjIod2bTMU46onZGv7tGpjLameSll89x8XJGXkz44KZj8P4206LGIchtxdFszMwaYgvaBXGzKSdkNkebIqC8i6GKFGADCV2V9XExrbE1zlYIDxbFuzfvM8gVvZVL9JfW2Fg7x2xaEOuYwcO7WCOZlg75zBVWu9dIsx5HoxFbVy9TVoIt2WNQlIwmRyyv93GTQ6RLuH79OoPpBBV3GIxLBBMKbzFlQbF7SFcr8tvvcH3rBsWHb3JeKsysw3Pdc/jxI2rdovIJN7eP2Ln3AV987Rnefv1P+c1f/Yf8q//K38TWplGcPUUDupgC+uN/Fm9oIj3xRN7zU+KFT7/CcR4616m6M48S/mzOeSJu8M7jhGE2zTkcDCjrmtoaMILJtGAwnBBHKVIEgYP0YN2JIOmEGpnrZJ+cL0opT+8ZT7iaOXAmnUfFjxPYf4ZxPjrcRSeSL37xNS6e22AyHuO8QEcRSRKTtTKiKLT1qF2FqwrKqmQ6GVJXFStLgs+9cpl24tjfG1LkhtFkhC0mxNLjNMzGA370+g+4/sxVrjxzHQTESUwUp0RJjNYRSoSJwKY2Af0saqpZgclLbG0pi5pUZMQutGu0acY0nzDeH8BshuxlnN84x8paSm9tjY5eRS0v01nb5Eevf8L2wz2iVPDRg1vELTi/ukG/u8pKb4leluHHQ6yv8c6graOyFqQINQZC4FzQn5ZGUHsLvoZqhvWaR0PLL3//fepY02s52t02ZbyMsxXaV5y//ipKwMrKEku9LuPRgMHkPpfOb9HrJXSWUgqfo1tdOi2FiiSsrsJ0QGs0Y6cc0FrrUkwM799/RHs5DLrdPxoQr3fpnFvh44dDlpbPESeSJOtw78GAZ55fJfcWIS0REpltsLZxjl/69/99/sd//Kvcu3+H55+7gtIG5yp4TBApPM2k6pNFfNqrsLCgPw2NnD2CMUn8Y0LoEHE+ToAwv92feZ25sLwBepynLmEwLRkUJQDKOOIoxiqBAWITLmLeNkUseMoTAz3tlYFmXsv8OgWnHelpqumUIN+5UGz6hOPpbUpGU8atEePhEdNexsOdbT6+fRdTOnpRi6V+l163TbfXQWkoipx8NmYyOqKqS7RWmNqx0Y1pyz7jcY12lofDPYT2pGmENYb94ZB333mbdhYTpwlVVYWuaELQ7nTod/vEWiOVQscJvY7m5ZevsbLR597D+3zy8R0+uXmfOOmEMFRHZCIhcjmUI3xZ0YnWIT8i3ysgilBEyLpA+xntpAY34cG9Txgd7tPKlum2Ojxz4QKff/YS61EVqhicb5pTzTHe0NYofNKBpjFljrUlxhtGlef9e4+4uzNA9M4TZT1klLI/OMLVJWvLbfLphFaW4KxBKkVZ1WRpG+9BOY92Hl/XZElMt7+EsxXTouLgcMir167z/ptvELX7fPPzX+PwcJ3d0QAjfeCGZ5aiavPg7h6f//LLGN1haeMiux9+l6vRCiIp8NZQW8PReMyfvv6Ib3zxc/y7//5/wDivm+nXBiE0fqG1ximPuKB3XYzovJtTL2cphzlk8vgweVFm8KRQ+nFe/NMGMc8Rm2oTH5RDxhjqug4zQYEsCw5GiNBb19EgxCKIBP4sx9zg/ix1nouP/ayI/uk8pxNMpzMOd3ZYySKGu7vc/OBjJjNDpmOE8GRZwta5dS5ePEd/qUu3v8RKt0NZzMjzKWVZQaZY6fXZiycI75B2lelkhNaatJ9wMBpz9/ZdLm6d45kb10laGZWtKcuC8dE++XCAqWriWNHqdIjaHZJWlxuX17lxaZmvvvoc97cPube9xzvvfcijnQNEVaC8ZFYYpsUB5ZuGu4926PdbRFrhEWyeO8dPf/0LlHXNRzc/Znf3EQfikFY2JNGSndsfoKYv8tOvPY+qbZB44RqYXYZNr+m0Zr3BmgqBxXrLzHr2SviHv/vHzKyiF3dIWh3WNjc5PNgjkpbp+JAvvfIVur0Oe/v7TUuTQMncun2HduTZ29kmzZZBiMD1Sk+30yXv9Nl99IBnr25yOJ0RU6G8QziBVhFpBJGwvPjyFZa/9TmGeY2OPPfvvcPGSsJSJvF1hRIJg1FJXlkGe3v87u//AV/50texVvBo+xHL7YSlpZjFPrFnPdeTFqQ4nnG5aCwwh0zOPv1TIe0ZT3P2PRaN4cmIceBNvXfHgFBd1zgXiq3bnXYorP4MQcD8PZ62YSyWCX7WZ/Mk0f/i8XRtrdb0+32oK3Zv32H37gPcIIfaMRUzsiylcJbbH99mf2ef5eVllnpd+p0O/V6brLOKTEIT56o0tJYUSZETH0pEEhMnCV7F2BqGRxM+/OgOrc4SFy6dI2ulZJ02WmuUUDhjsGUeerHODkCOUElKnGl0knL1whrnN1a4trnMW6+/we2bnzCuDWmkGFdwsHvEZDQlUpIrFy/yrW9+nRvPXUMmkke72zx6kLJnw4JebSvWVvokztCLwRUTlDGYyoRdtQlFRFO14I3FmtD+MqQkikkl+I0/eINPdqbE7VUipem122SRYrmT8sKzz/Lg9ockkSCJQl+h0WiEjhOK2jDNC6bFjGkxJdMZhSlJk5iyyKkKT1U7hqNDrl57jsGHd3lw+yO8inHOhglWzqJ8GID74ObHdDbOEcWK6eiI1d4SsayxIoztm5iC0jlGsxlvfO97vPHGuxgr+Ft/81+jE0v6vdVGNxpKoaSQxyjjYrA6L4+a52XzWxeW56mF+rh1eSJqX7jtsZ7yz4r0+qbjXRjQW9f1/ARQSpFl2bGq50TFtBjALpz9gsrpabWYZ3vdLj7/cR72ScfTO757TV1a2r0lpsMDBgcHZHWJNlB6h7cFUw9CKkxumR1N2Y4EXgkiFP3lZfory6ysruDKgtoYiqIkzwukg3anQ6vVZrnd5XBasb835OYnd8nabdI0Io4kWQpGhN493hi8c9jKIIQjH00Z5gXT0mBFaNIVYXhmtc2Lay9TG4+RnmlRcDSZEscdhI+I0oTp8IA3fnBIUVUcDobc3X5EbR3L3Rbr7Q4by0usd1tc2TqPqytqkWNFiakMUkRIIcOMkGZeZyVTbDFE25yxjXjrwZA/eP0mGk8iJaYsyfMxXhj2Dx7RfuU6N65cJZ/krC2toPFUlWM0KZmODtG+oNtbArXNJJ/iVcxsPCXWiuk0p64sRG0OHu2znml2tz/Gtdapo4xMlZhpzqDMef65K7SNx0U9RrMhKslYWumRJJK8FBRVyazIiSPF8PCQ27c+4d6t+2xdusYP33qLbivipZc/34Svc4/VLLTj4L5ZlBAet5CEzumEU4vyDDh0ytP4xdfzLGrtvT9pT/k4akec+m3BS8uAslsfJKbG2ea1LZmKAn2lBKFX0kknhMd5utOVJo/xov6kufVxZ8BGA+zPIMCfdTzVOPPxiB1XsDMYsb68THttlU5WkaqI8XTG4WTK4axgPJsxK0uSNCaKm3kapeejD25RCIfONEutjHaWsdzpsrK0RlUWkCToNAETuMPBYMgPX3+Dd99/h04rJRJQ5jMOpxWHowlVVWG9QykVxng7R16bQAmgSbSmn2nOL7e4cW6Nly9ucmlzg6X18xTGUEpN3FtihuDW9kO+8/vf5dat+1QWnBRIJVlqdxk82mdlc4UXr17kZ179PHb4gMoW1CYnEhqtdBi17mzjKR3GGaS31EXBJ7tT/vHv/ZCjSqOjhCiKidMU6x21rbnx7LPMZjmRc3hvODzYxxmHSCIsNXEUkwpPWRla7Q61V8zKmjRLMVVJp93CxjFVIQPw1m4xGRbcerhNtrSJNVEoRIgkb7z3DpWXHByUvPba83Q6GUV+hJJtYhVxNBwhvcTkOXsPH4QKmliztLrOd7/3x/wH/87fpixLsjQLVRRPCTFDBPv4xbsYCp993ONeLzyJx9Kr8kmyuoXjZB846SpknaN2JpSCifA6rSQOxjinH+eiBnGy8TwuhD/796nrX/Cq/uQBDZovTn1mTzue3n3PlNSTijfeeZ+XX3yOQWUpphPW2m3WVttcuLjJ3Z0DPrrzgKNhzmAyJIoj2nFEWyUoPEUzB2RfQLfV4tL6Jr0Ll6gF3Lt1m82NNbo6xgiPl57RdETtE4T0aEJSrnWCMRP2j8agFZXwzIoCM6+dExpEhJaa0Uix82jIhzcf8Sed97i+ts5Ll6+w1GljI1i5cp6VSxfZWFrni5/7EsZF3N/eoSwLsIbZZMyek0xLS1toZCSxNsdXFmc8IlIgGs5KCBCeujYIM8Xh2C08v/GHr3Nvf4SNWmjdxUYtjBMolYCFRMVsP3hAO464dP0as7JieW2NR8OCojasr6xytH2LujLEcUpdWtK0TZql2LrCWUOhKhINm0uraFsAQ4QXtDo9ojRDFIY4i9i6tAoqpqw11WzG+so6qSoRXlBbQ25qZBoxPDzk3q3bCClodXs4JNNZyff/6Ad87sVXsNb+mXjKs57lsXnjYxb2p7wrnzaAz/RaT3psE6R67ymLIuSbTSvNNE0fmyPOC6Q/6/is85i/1p/lcWePpxrnwegQISW5NewMhmjhMfmMQadF++plrl+9iuosszuccZTnjEcFlamw3pO1E9qdlCq2zEYlpXG48QzNPr52OBwPDncpcVxcW8dKjRE1IpZU3nE0mbDa7XPjmWcoixzhK6pigEGgszaHwjEYT/A+QuARvkZK8BKsFByV8BDHx9N93ng45crKKv1Us/VozIuFIl7qE6mUtbUtitoRRc2cFFdR1pI46tDvLuHrEdYMwVZIJFKrBmQAfOBihZJYV1DKmDcejPjjjx5iVBchYnSrQ5S2QSUMDkdcv5pw95NbFJN9Xnv1JfK65nBvh9w6os4a491DVvoZtXMY44h0zEavx2hWYuqadqvD/uERhghwTPKC9U7M6soKe+UMgafV6XD31n2GBwXd9iWMHbN57gY/unOfXivhKN9nY/0aRV4i8NRFwZt//H28MUglaPeXSDt9hNrjzR+9i1b6mJcTInQZfNKufxqN/IxFecYYn2SYT5MHfub7iLlSx1HXNdPpjPnYCCEEcRzalhpjQleCk5OBUwj1k99/8fzFwuPPhsKfBRKdPZ5eMlZXCBVhZjmTylBXJd6WDKcZQmgGlcJUjqIySB2hIk3lLQWe3NQoHeL3LIoxZYETMCsrJkVBlESgFLtHR2gVUZma4WyK0jFRnASgRUfMjGVY5JRKUSKZlTX9doyK2nhKjAApgtbVyRorJEpqQBKVilJa7hvD8LCiLTzrw13a62tczZZot5e4fiPl8o1ncFXO3Zsfs/3gNrnxTKaWTutZTH5IXR6hvCRNWugowlmBkCIII6wJJWESHo1y/uE/+xMK1ULrjK5KQKfouAUyoixq3nv7PZ67dpGJy0PnBAQqTuivrDB1IZyc5GM2z11ASk27kzEtatIkpRSho0JVeko0VWG4uNpnOjsgL0ZoJXCmYjoYkUjFuXMXefDJLTySLOqxtLLK3mDE5fUlBBJX12gHn7zzPnvb22ipiJOEOMsoqgqlE/b2jtjd32NjbRUhFM7aE7HcmUU75/uesJKfcPNpkORphvC042kiCecceHFMo9TGYrwkaSXHBinEAlcJTe57MjbhSRHCSa58QrGdnM/jJQiL12qtfWyPP/isKWNeoZFgXDPL0lMZ2BnmWL/HztGElVYLnGU2HQWOyENtDCNfIpRCSE2kU5S2oeGyh5mp6LVi2u02e4cD7uzs44RlMpsSRRlra+f42Z/9Ob7+F75KHEnGecnu7j4P7j3k3bff5cOPPkaIQIfg6zApzHukEUglcDgEoD14aUB6iqpGqgQXd2ivX0V017lyfoOfe+V5Wu2E8dEeb73+p/zqP/nHvP/+TfJywnI/oZyWCJfgvEUISW09AklZV9R1OWfi8cJRlAWzvCCKV3AqxauMtLtEpFKsEXS6K3S6XR7tHeDNBCE9kYJuO8PZwCfGkcLWYVlMqxoZSXQSY51EicDHae8pAeMUs/GMtf4Kl5Tm5oP30a0eutVBtEsuP/cMly5v8v5797i3t09/bRM/sfR6Lepqiq1zMCXbd29jjUGoiChpUZUFUk9QWvPw4Ijf+O3f4m/9zX+dhLnKwDcTpeGYlH8aJ9nkYU8zsLOL/c8TBh57q8V8dg7M0LBdtaOeluRFSeXC+Uc6Qas4CAecD0OCj43z+J/TOXLDT7o5j9vwo8fXy4m3PBZT+AYn8w7EZ6O98+PpDb5E02ZDh/4nUkhiHeOcY1qUxMByp0Wv28VK8KMR9XRKVZeMyhrrPHEc4QUorcP8kdqQlyVpHZMmKUppBuMJRoBxnpZWfPUbP8G//m/+bVZW+iitMNaQxDHFeMqffO97/Oqv/hpvv/sO+TQiz0OnBh+2SKQUaC1QWtLJWqx0e2ws9Tm3tUm332d7/4B3b7/PH731Ove3H/DMs9f4+Z/7GTbXllnq9njtlc/x0Ud3cWbC+koPb2qUgzjJkEnMdDIDDBKJkqLRY4Lyjm4as9TrMiwSXLJEK2uTJhpUjEpiau9J2m0yFVHnIUyVztNNMyJPKMnr95janPOrPbwUDKdT2r0lcIKqNmgRUVSG7eGIdhpxsH/I4SH8tZ/5Orce7HC7dMh0hVrsE6VTtB3w7W++xNQYprVhNBZkvTZHkwFOZnz4zlu8+cffR0cJVhC2NefJp1PSrMPRaMK7H37EeJYTdaMwEezUuPfT9MKf53gSX/nnMkzx6XoUvxB2u8ZIhfeURUld13gB1nmiKGo8Z5OTukXP+XhP6X3oemEX2qC6x2woj6NNHJ/O20O3xscfTzdOJMbUWBlmM2qpSOIoWD2Wyhr2B0fgQ52l9oKWivG6aW5sKozwpGkSumvrCO+CB7XGohMRJnpRNd23A9n9+us/4D//v02QSmNqQ7fb4eL5LfpZgilmfPNrX2B9uctHH99kNp2SxEEQYWyNc4YsS4hjTT9rs9Lusbm8xtJSn6jbxqua199+h15rhZeeu0y7k/LJRx9iZueJleLOvQfUzoD3bKwukYgwW8OUNeVsGsKQKLQVERAGpEpFpDPWuoK/+Qs/wf/1H3wPpxKy7jKiGFN5y+aFTfrdPof7u9x4+Vke3BphbOB/1zfXyPOCshgjVEwmPZsrfcbTkpEZN0N4HVEcU+YVlRe0e31WWpKXLpzjzfc/5sHuAVe2Vpk8LJnd/4RWucPNH/yQFXJyvUXc6WE6axgrUXqdcV6wc3+H3/6N30ZbT7uXYQgRRlEW9Jf7TGcFoLj/8IDCeAyeqNmkhZCNasgfW8afF/T48/CXTxUcnMn7Fr1SKIb3OOeoypKqCmvNI9DNtO7F5xz/PKFRV7jvyee+aJCfArTO8KmflZM/XfguFaWH2gSwRUqBcBIJVN5Re0/tJLOywNQ1wjliqai0pqgrKm/DsJ6SYJiErgFFUTKLFHErVIDEiWn6dwoElt2HtygnA86dv8zS0hppnFIWJYf5kBuXz5PFMe7KBVY6HYSvEMJTFDmz2Yx8ltNqtciSmOV2m6zVRkYJMo6RynN5bZnOqy/TynrUQrA7POLtt17nT3/4J5jKcnB0hBGez33uJdZXukhzgMRT1QYlPJEONameCCElzjqE0shsmWw65NXNmL/xjef4jfemOCvoZkvU4xmHO3tsrK3R7mRY5Vi9sIWUgto7jA8AmXWOfDajo2okjqos0EphakNtBbWFWVkQpwmmdnSjgICPSs/HDw7YaicYmZOrlHjtGca14POff5mjnZoH4wHXtq4x232ELSZ8/zu/yff+4J8zPDigt7RMq9MNuudmzksURSSJosJz5+Euh8Mhm2vLOFsiG5ohhI0nvOaTvN5nVawsLtZFdPcs0jvvbPdYYIXHe6v5faErhTseZmvdfMgUxwDXKXAnnNCp81g8X7lgaKe8ajMoafE8jx/jPx22qx+3+57CI2VTUGwt0oeZFFqHeZXW1eS1QRU5nbSFqXNo+nbWzlM3KbLBo32Y7ISQWAPOWBKt6bZb6DgiTVPSJKIoZtR1xcpyn16saCnH7sP7vH94gDATDrcvceXCBVpph8vnN6mLI8pihkvbjCUcFCXSWFZW+mytLSPbLeoownqPcoZOFJP2elTGcG9nl+2dPXZGQ0oHZVkhhabdaqNUxOjoiPWsRHhHHLewvsL5CrxDCIeQCt1U5xeVI/KOrdTyUy+e4/bhI25NCkhX6XZiKud4uL1Nq5XgrEepmFk5Id/Z5vz5NUwzdM54z3A8oZ73HzIW40smRU3tQMkY6x3dliaTJa+/+QY+brM3NTxzYZ0rVc3tR0esLJ/jcDfi9q0jpO5Sqy739qZEIuHdH73J9//g9yjGA7rdDll3CetrqqLEG0uVl0CQATplORqMePPNd3nxxtVmDqZfQEGfHMqK45x04TGPec6naIxmJPuczJ9P4jolMA/WsPiynzp8E9I6Ai0ymY2ZzGYYG8aZJXGMPmMcc0OSUp5CXuf3zcGlk9sXOdIQHodRhgtn1HCnxxiRn2stgld/0vEZHd9rvKyprUObIHfywmGEwwsNSlB4C6Wh3VV0lruY4RBtNVJanDHUBrwzOFEho4xu2sILhfCGapKjI82rzz9LlMbcvnOHqrTUlUcSYYqcw8kQ5xXj/QM21le5fOEqFy9coJ0laCkRpoM1FdbVDAcjtJDsPDrg8PCItJWx1GqTKonFIqwgVxIbOZQ0rPRaWHUO2i12B0OyKEJUnkS3meaeP3z9fda+cpVelGBRGK9QOkZ4gbOE+lapkQi8zHFRirWezZ7iWy9Kdr93h8r2QEp6mxtYpTg4PKKv9zm/3mUwnGCcY+fhDmmaEcU9NrrrbH+Sk7aWyIzEjsfUxiKkIFKSJIkZlzlKe9bWltBJxqPtEWlviXsPt9lY63Pz1j4XNrp89Us/x4/euoVVFZNHBe1Oh06nxc3hlFe+9HXeeuMHlLMcV1eU9Qwzy1FegWmIfm+JvCNJEt584w3+zV/8eYTzOAlOOLyUqCeOEltAKxc8j5sP4Q3IyBNz1VNebFH8sEBXzD3V2eO0sQus99RUzOoppTUYJ4iEJ0sjkiQJBjoXESzmiGe96ZlweX4sjpZHNF0ZxOJjGpjIyeNZnn8WKd9TjXOllZDbklntcd5ifYSQCm88UoVuQN47jJLkpaG1vETPg/RjlAQ/8xgatsgHRU1el6RRgkTjiUnjjG7aQUUSYRyT0ZiiCh0HXnzmOlevXifVMfvLy3T7fa5dusT65gbWOby3pLoD3uAwtJb6xJ0OVmuGRyP29vew3tDpdVFagnfUpqKqayLpiZQglh5fFri6CrM0hMWZMa3eBjsFfDzVvHRhFT/ZJ44VYayCDCVjzmOdIXgZR5n1MapDzx3w9fOSW5da/Na9MQ6NbWX0NzYRWY3I+jw4GiOiNhLJpAjTlpXWTIopWTfm1p2bJFmHbr/P3mAY0OiGymgnEYn2RGmHnu7SOarpt1ssx5u0ZcHs0UfU+Yv88O07oCN2H97iq6+8xid3HtBaO8/65hpFfkRvZZkHwzG9jqKeGqxzxFGEiDxxW1LbgkcPHnHx6rO8/e77VLUj1Scj/+ah5OJxakE2RvWpBc6CoZ1doJ9lqKdySffY+xaNZf44Ywyz2exY+ztv5vyk839cHrp4/+LzFgUaj6OFjm/zC+CPkJ9677PHU41zlk9DiZQI0qPaGrCBdPfWoJREitC062g0wXpBKiSJijBa0Ek81jsiAb00JdMBYBKC0DQKhbMOW1a8dv05rq2s8vH9Bzzc3ePh/QeoWc75Vof185tcu3QBnSUsLfXQkQ68JhopJFrFeF+jo5AH5mUJAqajMcPhEc5UtFpBCYKz1NaghaLfa1PUNbaaUuVTlAzd1zAzMDkrqxuMRI8D0aeTVPhySKwU1oWdUWrZlCM5vJF4r/BaEWlJP675uS9c5tAPuHukORpNMR1DqtsI0aLb60DkUKKiamZ2Su+wdcHWxgppJJlVJVYqjLNAGFcnUXQiTb+XkNeCw6MJS902xWTEg3rMc+dSXnp2ld/+jV/jha/8LOsXW7z04ufJJ0PW+ymTwT7dbsbmxXO85j7P3vYOdVVTV3VobqUVSa9Nb3UZiWRHCiovGeWG7/zBH/ILf+lbeG8IhBWc1dedhJzuONybh6XHud3C4n4sh/iYtXh2oc9Dz8cBRY8TJ3jvmUwmTVjtAEmr1UJrffxan7qGxXNqXudxXO5nodQneemJlw9v51H6xwSEdqcFeawxWoENFf7Se6JG+O2dDd30nAcV+MhaaiLjEDomSyTCG86v9rl2bp22EtRlzfb+mKNJCTiEN6Sp5PzFDWpbs3xlk9w6bt+8SVwZussZKlO0khZRkqDTCKEltgpNtbSO0CIEDlJ6sjim22rRbWfYuiKLYyIpGs4z9ECSSpCmKZc2NlleXeXReMjBZEYUpaAShCtpC0dPWLSIeHhYsJZErEdtrMmbAmsXCp8JzaSd1CRYYhym3cPqFuu65q+/3OYP7zkemoQqLrh2+QadKEEIQ+kKet0enVjQSlOGk4JqOmb94lXaacQn2wc4JVlaXsZaUELjjEMJS0tHDHLD977/Q77xtS8zmhbUdc3OwPDsq9fYme3z9g9fR35QsrX5DL12SiZGGDsj7iY898JzCG+5fPUqDz65i6krpBIkWcby+hazwmFMRKe9hvWKoqr4nd/7fX7u2z+JFmEe6XxGzNmFGBZrmNIFnAJJFhf64wCkU5TIY7zi2d8/S30zj66ttVRVFb43FQxSqVAN9KTw8uz5nf170XM/DoFdfHx4D7Pg3Y9p0iceTxe+O0Fd2WMuyckAWEjnEdI1NXECpCLM1pJMqopICCJn6XZatOMOy70OOorY3NxipdPn2njKJ7dv8+DRTtPZTTIpai5eu8xaO2Foh1x4cZXZ/hFi5jGlJYklURSjVYJEURcTqqKg1e8TJxkCCU4jYkUaxURKoXUYme6a9hPCe5SzuLKCrEXSSklbGa/eeJai8NRkVA7Kcsq0qLh95w5OabrdDkNRMesprixpUjvDAMbFOByyzhFCBqBHKGoURIp2y3F5FVwc8fd+7yZXvvwt+q2SzHkGh0dcfmaD85c2EU5SzRz3PtlmeHRA/6tfoChnJFkKkUBFETv7A6pKUuSCcnLIJd8l1h2ef+YaxjuiThddZwwmh1xLV7iwNebac8/zez94l3c/fkAr01xfU9TlkGc3XuDwaExvaY1nn32OBzfvYCtHlGiyVopyhr2H97h4/goPHhioLVmW8ehoyCzPWelEOBFRewmL+eDiIhfzihTXRKoN7QKnwuLFhb142/x4nJdaDBvP5m6PC1OFdUiCuqooDd4LlJSkcYzSIbw9693nr6WU+pTxzoGhx8sZT85pgQZubn+Kguoxx9PrOWl6nIiA0jrAWA+lCYtGyeNdUURQVUEQoJRCxhKPIy9yDkXYImblQ86tGtb6Ha5duUyv02H74BBTGBAR7e4qM2Uoy12S1NFeTahVTVGWJB5arRZSSKqiYjQccXR0QBpHJFmGVrLBhiW64Us9IrQ5iRIMoe5yNBozmcyI45jaWlZ6Xa6c32Jnb8Ct7QNcZSnzGdNRRW0dKo7Z3NggSxM+mTkSvcTldoS2Bd7kQbDlAyLpm768WkmksFROkKQRl6j4yc+t8f2771KUV7h2YZNr19dptzNmo8Mg+fKCGzfWuHc359HuA+o6hLJpmuK84Whnm4PDgrwQOFtxbqNDS8Kz16/wnT/5Y9a2zjPaO+DBB2/z/KU1Rnv3uL62wU986fO8+8F9cDnLfU2cLWEQVLVDRwmrW1tsXb7E4OgIHcU4bxkdHaCzNu1Oi6W1ZazVCCWpveDR7i79bBPnA/nu/Mki9Y+hJE6tJvHpbgqfMsbF35+Qfy7+fJZ3E4TO8lVZURuHlBG40Amh1+kSx8mxYS6GymcpnLOUyfzxcEKHnGh2n+TR5+tk7l2f0riazzBO75qmESrsggFGbkZ8n+npORcOzxPwoqyoiopEaa5efYZet0tZVAxqg5jkrHXarG9sMpzkjA4G7O7tkT58yLguKcSQ3rKAqqDb6SB8ymSQs4RHS09uKvYPD7l3/wHdTp9efwmpJQiHk544SUjTNnI0RSiF9VCWhqOjIw4ODnC2RkjNLJ+xutSl32vxzLULWCl5uHuAlBmR7rG1tcnG5hbdXo8sSxFJm1mSkcczoulDlJ8RpRm51UghiGQYWW7mG5YQpHGKLGteu5zw4aMD3vzBu9y+dYuvf+1VNs0lLIZON2ZtvUOZ51y6cok0XWJv9x69boKoNa0spRyNqcZTYpkxnE45OhoztEOIIiyOBw+3eXDzHjb3/PJv/Av++r/0Tf7BP/oVSrkBusOXPv8Sz9zY5Obdj9nff8Qzzz/H0eEhUadNd3UJZDNDpIixQtFZamENXLxwiUfbB8hIc//hNh98dJNnLm7grMFJiROn87rHGc3ZRX2WrD+15s7kiWfvexxA8zgy/+Tvpo7SBhFCmADngwhGa5SUyOZnkZ88e+6nhAjQdIdc4EB90CnNRXuPA8Ie95o/tghBNojkYgIuXHjBrNVCAGVZnv5AXQCBnBDgBFEcczQtqXyQ6xli0s4yla2Zjme0l5YY7Ozwq7/+a9jf+A1mlaf0E1545TI/+9Pf4sIzWyydX+P+J3d4tLNNmGqiGIzH3Lx3n/W1TbbObaDjBIRFRopWt8vq+hbj6aypoYTd/QH3Hz5iMBySJTFKjRiPJ9TLPeJYc+3yJpvnN5lWUNvQmjNttdBRjI4SlNboOENHMXliEErjx3cpZ2N83A6jEoXEGHfqw1dxguplrBVTfv7VPge7H1CpNX7rd98kH3+fq1evcuPGVR7+3p8yHg/otrtcu/oMy0tdjqY5Tsf4SPLCi69x/8E2deX43KUvYb1jfLDH4eiI555/jrffu0mUdWh3VqhUxaR2iDim1emR9Tbpn9siaUWs9ttsP7rL5lKfw51dyrLk2Vde4f3XX2cyHKCVptVqs7V5gdFohlYJB7sP6KysI4Tjg5u3+fY3vxamni9wmIsGc9a4HlcH+rTjs5Qz8/d4nEGezf3mtIu1FuvmnRA0WmuSJD4FBC2+3pNuP/57zvf60N7SOgs+THk5pbV9jIHOP6tTFMxjjqcb5zxuDmfUdLduqv+do9/roZQiz3OMCVOdVSN2V0qBlDghGUxyvEoRMvRm9V7hhCNpt5mNDcNixuHgCKUSnE+oKse993cYvDDjqDUlll3WNjcYDwccDQ5IsjbG1uztH3F4cMRslpNmAoFBCoVxgaNy3lMUFXlZs7N/yGA8ZVrWeO8ZDD2HR0eMltt02ik6yWinmqTdRqdLxHGK0lEAvIREoNDC46ViSoxobdGRNYzuI70BF/ZMKRReBqjeO0dlKmqlyeIWl7sjfuYLGf/kjXssLd/guZdWqOqauzvbLC1vsbR6gTgWzFzOYOeAdqvF3qxEygP2dg/AepI4YXbnDqPhiKV2ileCnb0damO4fv0ZxrsPeOHSErP9W/x7/8bP83/6v/xXXH/1a7z11ojrf/kn8dbw8z/zbcrK0M1ajFsdsjQ0ufIe4igKDbyRpElCmnXo93oMRkOuXb/G4WAY5stIfYy6Pg15nf8915B+1oJcNIYngTyf9dy5AYRcMKxZYw1lWaG1xnlFpBVZliLVp43wUx5/fn9z27yZ+vGsHHyQcQLCuzOVKZ99nU86nmqcmXSYBlKSVqCFxEhHhWU0maAjTbfTRQjB6GgAUuK9w2kapM6FjmeupqpnTJVnJepAJHFWUVeOUT5llI8paocwFqdKVKI4f+4CLZVQHhww0XDp2RtceeYZPnn/HWxdQFVjKsfe3g77O9tEfgWJQ0iJVBFVNaMuSvJpyWA8YzQpKKsa0UgKp6VjMBwxnkxQ0hOhEJFCRoo4y4LQXcZooXBe4L3ESw/CkUgQuotJruGNpVXu4q1FogCNFRKfSKRVJA5SFSguG8W8+uxlPhp8wJ2jHapxxMrqEpHSSG+YTcfsHc6occRJzOgox0tJmmVEaQrWIZVmWszorPbp9boksWY6y4k2E+7cfsj7P/wj1HAJcanH5c0+f+1nv8o//+FHVMvnGOUFQsdESlOIirjXwh4JXCFY3zrP3vYOw8Mj4llJXjiS/jLrF66gkw49l/Dw47scbi6R24ooS/HmZAF5OB5IhBANZXFSvRFEP2EDc8Ijmu9KeH8q51s0sMUFHrzOk1ZqQIeP/1qI9BwOj2Uym1A7S21DzZIWnlaiG8onrNUTNZPAS4HltHGJU/8sAkSL/K1sADDfPG7xsZ8GlRY99Nnj6cYpJLl3lCLsFBJJFCmcC3rbg8NDADbXN2inGUdHR9S1wViHsQ4pBLWpKasSP/HU3rE/GRFpTSeJGc/GTExNDRTGEEcR3huWeyuc29qi224xm4yJMoV3sL6xRTmb8NF77zAYHGCt4e72NsmPJOfXl+mlMd12RpwkFEVNVZnQIyfPqaoSZ2u0ErTimFjCbJqT54ZWKkBBK8vI2kvErTZxnIBQQUDggvRKSkmsBJEGrzTOtRC9inLgkOIotLJsQCmEREiNMTXSGkolsGnKivH81Rde5P0iYTdbY2t9nfWVVXYe3OfmRx/hZcnWuS3On7uA9JrtvV1u3bvLdJijhKQVJ/T763TaLVpaQW3oxwmXNi9wfvM8l65e44Xnr3H3gz/Gds5x6Yrm//Dtn2dmI/7xb/8mN65cxlvDaDRgWuSsLC0xPhzx3Kuv8O6771DUNcblzPJdOlYQJz0sEc++9BLelLz3wXv84Ac/5Ce+9lWyuHVsDNbaYzL+GEgxgfx3zp2eJCZCV/8gA226+zf3LVIbj6c3Pr1OAyr86ZD65MdRViXG2DBYV0CkdRBciEVO9GRjcM6dMs1jIzrmSU8Otyg68PL4GhaN/eT/xfN+etnYU40z9RLtQRIa7RrviISmnbWoq5y6qhgOh3RabS6eP0+v1+PR7i6zvKKua8raYrxjmucorZAqYmd3n9utjAub60TAcHzEtCwwziJtRZq0SCNNEmnAMS0m6InGGkOqe6xubHLzow/w3rK01EJrxWg6YTzYI8XTThP6/T5R0mJahvK02lZI4dDS0+9mJJFGWE8ct8l660TtHnGrTbu/StruoeIUFYVSIicEivCBKyGJcEjhqRB4lTIjI+tfxk8dVDmJTlDGoaUCK1FZB2xBFQl8VdOylktpxAhLHoXHOwObWxfZ2jyHlhApSaw1RVli7QTJBqPJhOlwhMRCfYR2EJOStmOQivHoFo/2j0jR7N6Z0M5C4yppcv7FH/wO+yajsE0BvBT0Om1klnJwOGJvOmVpfY3rL73Ag0/uoJwiTjoooYlVRNZfovaWq9euI5ny5ltvc+38OZ65+kygwhovNQ9Zw4J32Ea2Jpxo8tO5V5PHFMVjQ8jHAD/h5jktczbnfHwzrhODgaqqqOsKrRROhpwzjuMF2uPT/GXwhk3nvqfYyYnwwoNoxkp+Knw/QWrnz/mfZpwotA+5JjKAPVVdI72Fpk1iWZZsb2+zvrrG5uYmtXOM74Z5nEopKmMQ1hJ5jXaC0pd461BSkujQixUn8cJTu5o2PtRQ4kGGkDifzYijlDhtsZqe4/qNZ7lzb5tL59fxztBOIqppTT6eUlWG2mpaXUXloTImTB3D0W6ndNspVy9fxqF49XOf5/r1Z4jTDCk1UZwgtUZpjW9CFSkbjjR8tEFv6QzGO2rpMVLT6q1jkwp/tE/ZbDRiPl8kUfg6IXEOBdSyhg5caSXYQc5oPIYoRsQxKoqb5lWBWslSxebGOdrdPrPZjHJWEElJmmiSVKKlJE6jEKoNPHZ/RlvA5c01kk6fvf1HbPUy3vvwQ9TG88RkrG+sE6o2HbpBmK9fvoxTiruf3OLR7QekaYtWp4+IWxwNDzk4vMfVZ14iL5fYuHSOtQvnufPwPjduPIdoZGhzCmK+KL0Pm1gg2+chafOZCHX8PHiMoH2+nBe8aDCaJzXIEswB07N8pPce6yxlWYbKFGvxXpIkCa1WC6XkMRVy1mOLed7q3HFOuWhYi8cc6fWCsGY+BfiEiqyzRvljA0IahRVNMakDKzweh6rN8Y6EF+RFybvvvkeSpCHxzgt0pImTmKIskTi67ZR+u48WEaPJlN29PbTwtJMYNR+YqgDv6bYyulkLiaDb7RDJiPFwQG9jAxllLK1vsrl1Du8lq0t9vK3Z3d7GlpY8L5nkJVHbk6QxtdFI1aLXbXNuc53Pv/ISm1ub5GVFd2mZzvIaQmu8D60u3fEm6IMsr9FpW2OoqxpXV0EC6GBc1mRZgl9pIVSKjXp4o4h0iZdV+EJUBCh0WSMVuFaboi7oGcWr/TbvzQ6YjCv0yibOpQgBUjpqIdFEaJ2RpZI07eB6oTIoSeJQUN5sIPV0ilKOSxdvUJUTct1GJH16vYzq6Cbf+sqX+P6DET5WKBFkmHVV0+32kWjySc7DgwGvvvJFqt0DDncekseSbGWFWEogRVLT77U4PBjxO7/7Hf7P/8f/PXOiPfTCFk04f7J+lFLH4eBJHrjQIeDYwGxzX1jEwdzmL3XC5AsRMTfwOU94wv2f0H0nxjs3GkFRFFS1CRJFB0mUkMbpKcOc/6+UCga08PviY8565hPwaa6MasJ670/NHvULefjiOT7Z/p5ySCOPC62VcRgFXjpiE+gM3zQXds4znhVM8xLwKCWJY03oA2pIlGJzqUu/3Saf1eyXEwprkQiSKCJNYsQMTG0RKWRJi7IsmAyHZJmm2+lxeLjPWnmRKOoQdZbZ2LrEo7vbrC6tIgVMxwUHh6NAnRQTunWLfjdjaXOd85cus3HuHP3lZXq9PkpK9HhEFMULn1r4gi2BD5sj0jIkIhRFQTkdNU2wYkyTh3ZXl6htAM0+uvuA5bTNudWUSFjqyqF1UBGJVgALbFURuRYSjY4V1zTcLqZMJjk2SRFaoBKBaPhDrRSCJCzu6ER5EkUqDNwxljTO0GLGUqeDX0poqYg4EkxMgTbw7Poq1TufcOnSVTKlSdIUJjO01NT5iKIo6GSaK889y9fWW5jBLeSl59ieSN763h8xsctErTa6nrGapSy5DXppl1jpUGUyR38aIGhuXGEC9GkwxDe9eYT0zUiExxDxgscCJeJTtwUPjQv8ctC6yFOKHe8dxlims5zK2tCQzUsiqcIMHuefyG8+mTudL5tPh6nuCSMcwqYhAw/aPCSkAj9mPWdLQIwn9775UEF4ifIBILJnwghEGCnutQxwc+2IvWK1vcJya5lYa4b1lNLUWA8yiqhsTWWCnKqVZHSWehxNJ/zgR28xK6+x3G2zdU5Sqph33/2AC9euoaRmdWMLGacMp1MuXrxIb2WEu38f25QIjSYzlnvLXH7mMs88+zz9tVVEFIGSFGVJbiyVKZE6xjrHrCyYNSMcTGlCB3fCbI2yLBkMjijyGUv9HlubFwLNYirOrW+Qz8bkTtM/d47J0QH39kZkqmZreQnpK5xSVAa8V8RpCysdwoYQKsZyQyU8GO9zgGMa9VC0SGygmxbVKd57lFLoSAWUEYEQQSsaRREyjjBSkQhFhGfmBXvjmuV4xjNXLlGrGLwMG4H1TMZj+v0uQjgSleLrklZiuXJ1g9Zmj5FN+Qurf4HtowlH4xkqydDxCl5EdNsJ3plGfvl0cGMxr5sDJWGUvTj1mPkaOl69c4Of/+7nnuYxeVuzWS4aSwhjDVVVk8/yUEjuQngpFpqCq4XqmScJAx4nmjh7zVKqE2/P6XOZ3yqa3HwRUX7S8VTj7GpBah2jJtENlg9KKGQUBOdzMfE8xg5NZjyuNqQiIsvabKxusrV1CedrDiZjVKTJixKNZg5it7OM8xcusHnxIrdu3w5d1tE4FZN2eqyubyGjDFN64nbMyuoGz77wEjuPHuKQqCRD6BgZp6RJwtraGusXLrK8sYmIIoqqxpY1SkdUdc14NGV8dMS59Q10HDEpc45mIySK8eGU7e1tiqIIE72lwnlHp9NBJR3idpdOq41UkGQZs+kIRAoqor28hChTbFUzM5JYlGipUUrgvcJYidBhgXlnSaUgFhKb1kzGjzgaHxCvXEalHZTyTY7kieP4uILCWgcuqJnniyZJQg8ghEZJhXY1QiimRvFwd8jzL17m+29/zMb6ZaqqJE2TplgAur021SwnXlom62uKg9vEM0fWSSCRbHYiUq8Y52OWlzt85Sf/IlkSYb041c7jccdiSHi8GIUAsSCEP/5lXlp1elHD3POo499PGco8FWm84FlDcs5irMHMEWUEcRw17Uo+ndvO3/tsbvgkPvd0mH22+PrToorHhcePO55qnG2l0HWYveFVgJ0V4UdGEToJCyYgYWEGhZzrcfG0W1mA9y9fpd1f4eBwG+sdOorwZZhIbZylrCqWlrpsba2ztLLC0XTCbDJmOMs5t7XJ+UtXWV7fxLhQHia8JopbXL5+A+MMSavFuQsXSVstjDEkaUocxyRZhmplTMuSsg6Tua0JQ233H+2y8/AhdjRleWWZvdER43pGb2Wd9XPnWD93jjiOaWUZSZqSpileKaTWTRLqqMsZZVVi8cQqYfvhI86fX4WkT11bHu7tsb4MbVUgZITSoUWmR+K1hbxAWUGlLKpl2bKWuJQMZxNmcUJbBrphvrsaY5rdN4S9ocZTorUmchGmqlBJjAYiTOBWZYvKVjy6f5dEa+rK0Ov2meYTlBJEcaBjeokmFxavJEVlaJeGuKNwQmK8RMqYuhxzsL9HXsyIvcN6hTxTbfI41BPOgD5Nx8Lj580XfFixx95ycVEvVraE1zwJ8UNKspB/Lhzeh24SRVFgTDDOSEckSXpclXL2vRbzyPl1nbye/9T/i6HwnOuc0zKLhjzPQcPjCLLCp2xuT+9bS8g5y8ijpaBdhwlYtRRo51DOkaUZsY7I87yRSIV+OKmI6aRt1lfXyVodRvmM3cEBo9kQpA8jDSxYJ3FKknbaRGmQ4A0HA27dvcdb737AH/7wLaZW8It/7a/QymKcLSkLw8OdPd5++x2YDEl1TNrt0u6vMp3NGI1HzCYHTEZTNIZza8usra2QZB229wfcf7iNxDKdTrh09RL9rU3Wrl8lSlN0nKB1egxmeO9DnyARRN61qanKEucNeT5mlvcQSuKUo9VNKadTOq1VSi0oow6DYoorj4iVJu1HqCjCeEBJoiRBVY5cg4w0m8KylMNH5YhJlVDpHqIZSKQVKCmCmsELQCEQ2KoCCdI7YgV4G1rLIEmFpBWnHO4f8eKNa3RX4bCYIZMVyuEI5Wu0gCiJkHhUMaXbaVE4jasnaDtGqhiZeYS1lMZgxxOqsqQ2FpQ6BXg8zvssGpRSKkQC+CAOmBtZg4U7F1qaKsQxmnuMkHuPM+4k7BWEYn+CYsc2NMuckGmwGax1mKqm8BafKGTtkN6hY4VToWLJLYTJcxVcyB9DH9oG0jpW/ghC1/+GaDnx+DaUDwaQTIAIf9umRaOdo9ZiLlxwPGZu05/NON/PhxyJ0Nsmpgm/hKKKGtFvM/pu/sEDOBd6tqAVWaeNiiJmec6kKoKWtQ4yP2cNghAuRnFK1ukwmk3549ff4MOPb1FUBhVFHI2m/N2/9/dpt1r8zE//FEoIrJDcvXuHv/8P/gEtJbl06QJREjMYDqnKGi0EaRSg8ssXNkmzhDRL0FnCteef4+UvfgFfFxweHdLtLRG328go8IXWepzPoTrZrUMbRM9xf1McCEe306aua3QkyZKYcxub1KMhEh/GS6QJNVCxjqlzImNoRQYfRViZUGNC71sUsRZoOUVqw7qIyCeHVD4izVrU1uJ04AWdlcyqGaWvSHVEO07AWaywVL5GuwijIhxQFTndVsaVF5/j/Q/e4bXPv0IpHJPphLX1Darx4XF7z8pDEqUYI+msbGHyI+pyRpa0OTwaUOU5wnlqU4cm2o2qRjUcxtwQn8Q3Lv7dOLqT/FHMBQihk36o0jkrJDhdinVi/A2bKk6Hx8cIsmzGTlQlxlmsNURS0um3SVsJSp3xcseJrQ90IU270OZS5o29mjdqrv1EsCAQIbPjJMye90SaG7SYn6MUZ17vz2Gc92KoVUKEQNsGLdNNHyEbQiwp5THcHBBOi1Agtab0lkcHe6StDhbHcDhkNJpQWY+1HiEc3oUJT7PKcPOd9/nkk1uUlQ07TVURRYLdg0P+7t/7+3R7S3z+c6+go5hWu4XSisJZdoZHnD+3zrPPXuX8+ib9Tpd2u0vWa9NKY+JIBc0tEhWnyEgynVVB+4unKEsoa+bfsJCgVMM3chKWCTRShpI452va7TZ5ntPVbcppRT7KKUZjuqspRBoroRQKr7u005LST2h5g/ICRIJotcErMqkQOKLMUqucVSsQTnN3vM1wYEBG6KxN2uoRJQkrnR5CenxdkwhJrGJMVVFJgZEaZx2RsvQzzW/901/lL3zhVRLAFznSSrIsCqGlktSmQquMNG4T6QQ8KAwP7t3kQhTRSdq4omJyOMDVhrIIIw2WAakUslnMTyLVF/O54xCw8VDHC1403nNueO7065w0+HpypYudGxYch40CwDl0JMEapK1paYUWgv5SlzRLwlDmhpEQNBvynEKTMtjo/DwadkKeoY3mEQCEaH1+6uG6FfM2qm7u8UX4zFyjF/ixjDPrL5F4T1lWAbmUMtAfFirrcCJM/DLGnPoyokjT6nQQkWZWV1TTMbPpjOkkpyotTjbgkvRkrZR2t8W9hw+5c+8BRWXnmyngsbamlik3b9/nV/7pr3Ph0gU2NzZY6vXZWF8nLyb87Le/xWsvPkMvi9FS45zAo7A+gAhCBlGdQuKdoMgLJvkMYw1xbcBDrJMQUimNjOZ5XvCWQtCQeaFfawC8PDrS5EXoNihkzNLKOruzIdZbhNKYGqIoRegEL9vMKkk5m7DcidDeITFk3RbYGO89lRZ4LemplK7OWN9wzKq6CYOjRiVaopVH+YDa9rIW7SxFaYnQitxaZuMh2lcQp1z4d/8Wg6NDejF8cvMD1FIoIUNHVEWOxgfgSQisFdQOWlmPrL+CNRZR13S7Xe7ff8DBwQGTwlDNcpSfh59PJ9IX18WxgTb849yYTiR7HOecj1MPgTummJpE9YSWOEPH+PksUe+QxtCSiq6MmgHD0NYR3piQBohQkyukCJ0ymtHWx0Z7vHk01+E4Lqc8drI0aKySZzrqLUQSNLNCncPjwJnjoPlxx1ONsxO1Q7zsJDKLUVoSG0cxHFNTcyxmWdgxpYQo0iBhOBmztrJGEiWUeR5ceFPxED5fx8pSj9WVZW7eukdR1nhU8Ap4cA5TG4R0IBVv/Ogd3nv/Q/q9JVqtFlsb6xzuWS5vrLPaSYiUw/uaCkFdV9ja45GoKAURhTBChFAjiqNQBhYpkigmiRKUlAih8VKFj3KRLHb+JDRxYSiOqQ3eh4qLJEmRSpGXJS2liGVMaiV1XhDFHhsnuLiPtZrZYMRG19NOHF4LFBovNdbHVMrRjtvEKqUdK1yLMFvS0Ww0ChVHQcnkPN5anCiprUGiiE1NSo6vZkhf0O61ubJyObQiFYIRkkMJkyonSRN8bfBC4n0YzS6VwmpJb2ODfPchPVuTtTN0EiOUpCoLRoeNjtifRBZ/1uN4nQhxLDVf5Eb9wmNOkf/BDR6LE0STl548y32KCgkhpaGeTnDTGVHtaBFSMmUd0Ty0nG8Cx++1sDEch7THZ9ts1uE+731wfv4k95xvDJ+mX0K95zxsCIKHH7fYuqWRlaOruyz3+0gFs2JCaXKi3OC9xVqHdfM3CjlkEiuqIieJWiFPKabkxRFahSlWtRPgLN3I00sixuOc8Xh2ogrx4UJc0x+0rgt0mrGzv88f/smf8NrnP08nzXj+mRvsZAlSRhyNK5y3aC0BhUSFGS1Ko3TUGKlGKo21UbPjy1A2JBVSyKBemcvKfAhv5Tz/cUExJAhDWKuqBCGxzlFWFZFUTAaHLHW6aDQ67WLMDO1BRwofaYRKECKiqEru7x6w3klY6taoqKJ2ElMZeq0uQihco+aUkQ79Y11D1vuA1NpyRjmbUlUFPtHodguLJ+pqPrx5F3dY4OyUYbVLkiVcP/8ClzducHd0yCg/opt2qMqgM62FJ40UmTIoKmZO0V7eYvzoEet1zvLyOVbWNpgZQy0Mu7u38NZgLGg5pxD8GTT10+jt/L7jOZXytCEACB88i2uS0mOPhEALffo1m5zVubDg58itb1yZaHJCU8+acfOGqpk/urKxjpAxSmrm7UOca0Ca5oWk942iyMFcvuc9KHEc+obnCOa9bD0GsYDynEQDJ+VyQobSwkUk/s9tnFJCq9thpbdMr9PF2JLRTFP78nixVFUVIH4hsNaio9Bw2tSOLIsRwlEWBUVRUBQlzonjD67X7bK2uspHdx5S13WTKJ/E7ItXWNcVUsB7773P4cEh/StX+PKXv8x0/Cyz2YhpXpJlCUonjVHGRDoCJFpHIIPXQQi8MSgbuMMojsMuLI/3tcY7zsmz4CmFksjGQK2zmIbQnkP1cbuDTFoMHj3k2QvXOSx8mKviKoyVJAKUEHhpidptakoeTcYYa+l2QKiYTruLjtsInSB01CCaYJ1ACIW1LmwkxqOlxvQ76KkmqmB/r+D12RF+dZml9Yt84cUL7Dzc47f/6a8zfPgmL7Re5+d//hdYW93EScWjsSXrLTHMx0TS4Z09BlC8UKgkxktNXta0hefyxUscjcdU+ZR8klPXJTpuhgcvGObcKJtfTgxFNj2Fms/3rMEuehl/DPqE6QI0G2lDSh4Pv55zosGnyeO/jzW11lNbx9Q4ahXh0hbWWFQUYUWEQWEsiAa0mXvtgKWIxjCDUgxrjxHk+elb2wjk3fzcg7jibNf3Y7DrTLh+fA0/jnH2l/r0Wz2WOj1iHZFmXbpVRmlyjo6GCO+b1iTiuJg2imKsCbufUgLr6pAPoagrg/MyVHoI6Ha7bG5t8cGt+8cnuvhFneQc8viD29/f5+DwgEsXztNqtYhiRZLFCBEKveMkRasUrSK0VqGTgY5BCox1OA+WkjgRZGnaEPvyeLe03mG9CZGHnSNtHJcazT/MsiyZTCcATU/UguXlFX7w3Qf017Y4mhl0FDMcDKjKgv7KKv2lZWprwnyY9jI+zniwt0MrH3JpawPvDPgaawVKhrKq+eKXQhznwcJZRpMRs1TQURGRjunIiFsf/Ih/+N//ff7ijcusfP45nl3f5O/8ws8wtt/mH/69v8uv/Pbv8Jd/6lvEok1Gn+FkxMbWJrPJAaV1OCFxQoFXGK9JO8uMBtvEwyG9lS2qvMYbwXQ8pS5yVGbwQh1/b4t1mUKGsNUjFppC++MIce6hzgJGx5lo8/gTY2i8aGPhp/WscxR1noIEw3CeUIiRl5RIaqWbEVQS0AgvQ2mjDCH9fEOWDRDU+M+GRltAhsLk5CZf9Ag1n8diT+E782ub03Lz61zcyJ42juGp87vjWGNMxdHgiKKY4ZyhrArAEWl9TIvEcYxzgU6Jo4S6dg28bPEY4jgohaI4OQ5jtNZsbmwwHAw4Ojo8RuIet7vMDRdgOp1y586dQCg7S5KmtDod4rRFnLZQKkPIlCjuoJM2MkpxSmOFDrMy0zY6aZOkHVqtPmnWI4ozlE4RIsYjsTQd+6QICf7J9xG4Txdkfe+9+14TdteU+YxiNmZ9ucvhzn0Sadm+/SGzwx3Wuh3ufPABj27f5tHdBxweDZnVUMgWfnWLQ+t584N3uHv/QwYHt/HVHma2QzXZxcz2ccUhrjhA2DHSjoEcqRxueMR0tMfYHpBllr/z1a/w3/3SL/FC2uXXf+07/O4f/SFR/pAVMeU//I/+lzwYSf7Zd/6EnQd3GQzuYtSM3cNDYtlja/0CWdomlFVoagPnL90grz1aBU3oqy+/hvQaUxlGwyHzJsnzBbiIoAa+O4Swcx4yfI4nZVhzDzP/OTbKuYqoebxrTNM4e/xaxrsgFZWiafAN1oUUy/ogLTWN95zmE/JyRuVqalehtaSTpqHFp5Rh9J9UqChCRhFehO/ceI/1oVzMidC8zQqJJyCwC3Dt8WnPq1ziOD7+WaxOmaukFsvrnnQ8vW/t3gEaSa/VZqnfobahcNo5SytLkEKQphl4QVWUIeSSIZ8kgJp46zHekGYpKo4RxqJxtJKYrXPn2NnZYTiaAE3FwqfAAALf5AReSpyH+w8eBsrGe6TUqChBaRXySB+AJmtrPBJvQrNkITU6ilEqOumN1Hy4UqimRXKDyjmBa+ZphHAn7JK2AR5MbXGV4+7tO3zlG3+Bvf0diionSySvvvIyg+GEWmVUs5zNlT579x6w1uvz7rvvcfvhNpefuc6Fi5dYWVkhjgRJd5lxnvPerUck/jaby206rZR2p0d/eRWpNc6GaER4R9JdIUp69PUK93d3+f/98q8Rm5pf+PrXOHdhjb/41dfYefFz/JNf/ae88c6v82/+9Z9nFc+/9zd/kT/5wQ9458FD9LphdW2LpN1lMJkyLnO0diitghfQkjjLiOKICPB1QTuLkUpS5obhYMj6JcvZdo/HBtqAB3OCQxz/Nc8hOc4ZQ2TQ+AlxvOTxzfc5x0JV49FgAYw55i3kCUB0Zu24YoYp88CRI9BxhNQiNEsPKBAeQv1pgzUIH5IcMT/bhTxSeL9wZZ5jzrIBOxePs0qis4DVj51zHh4Oaacpy70+7XaLVjvlcHTILJ8hpGOp32F1dZ26clRljbUOZw3eGaIoAS+YzQqEEME4owipSnxdsdpfQ0jJ/e1d8qJqKIoTyP0Uj3VMXguM89x/+JDJZEyvnSGkQuLw1uK9wTvbGKfFeXU8og9cUCQZEN4ADucqoihBNFxhEBxoYhNjTI33QZcJTXW8sHhvyccT8smM0WDE9vY2IglNl2fe0xZhvNwP/vhP2d/dQ1WGXqfD5vkLfPRgmwuXLvKd3/1dPv/qyyz3e1y6doN2ZwnRuYy1XcazA2598B7T/YdIAaLZzVfXN+i2O/TaHT73la+SjI9IuitcPneZv/CVb/Hf/H//a/7gO3/E5fPr/Me/9Hc4lyr+zr/8M/z677/Bf/Zf/nf8h//GT7Octtjc6HFx41VsnCDimNzMMKod5p5oHT6vSFM7ixUQZSmj0YhMSVSccfnqZT788Dbj4TgsfHeCYh4v3obLdN4fUxFneVC5QE0cP4cAsMwN4dhIG4OYH7LJO30D2Mxx1bmBL5gR3jmK2QRvaoQL+buOE1Qcg1bNThEQ1mCO8+txjXHOq2b8ydmIY+Ceud530eDmfy8CY4/rKP9ZFNRTjVP4UMforKEsCoRw5NMpruEGpZSBiBdVgzx5isKEiWRNq0FjDFUV7o91GL6KELSyjLwoOBwMwu4VovtTX2D4PVCM849GIBgMBuzs7LCxshyaGruAngofusDjQ/gh9YlxhpYhJc4mzazGCrzB1AU6ihY0luG7UfL4l2N43fqgza1MxfbuIx7t7vKP/tEv82/823+LqioZljN0qrDOcXi4zwvP38BMJ+Rlzs7eDr1Oi7aUrHbbxK6kpRz/4vd/Fxn3+PyXvk6atXFtRbwlqNNlRgePODg8YPfwEePXP2Q8ySkKw0b3H/G/+Lf+NdaXl7hx9SqvXNrkP/1P/3f8Z//F/4PB/R3+7n/13/Nzf/WbXLlxjX/pJ15kQ474o9/7A771E99kfXUF086ohWLvaEC2foGyiSK88yg5bxsZkdee/sZ5mB7hkpik1aLb61KXBfvbD5HGIhJ/vHDDApzXeMrAKx97tmbhimBoEo7Du7PR0qLRPk7YcMpTN/8vChTmIXMYM19R2jqkKs3EPK10Y8g+dGhwHuH8cR4s5z8sIs6fTrPObjpndbZPClvPam6fdHxG31qHNw4afWLu6mCYzclFUSh+nbe4d64+FsBDAE3mP51OhyxJmQiFl6HCw1jLrCjwUuCNbTzc2S9hrkc80Vnms5zBcNjwWEEK6K0B5gKGkBdaY5otTobSJiOxdUVZ1djaUFX6OCGXQp7KBWgW2LwC3jVeYpbP+OT2Te49fMDRaMi4LJgMp7R7HfLpgLEtmU6mvPjC8/Q7HVyiKIzBKVhbW2Uwy3np5VcY7O2TJi2eu7TJ7Xs7fP87v8HmhStcv/FsU92yhVQxImqhk5ThcEivXTEY5Xz1mz/NOFvng7fe5zf+2R/wt/6Vv8KlS1v8R//OX+dffPe7bFx6nv/ht/+Q4pd/j1/6t/8SX3r+HJ/YjN/552/x3Je/yGpHUhmLcAFcirTA+tCcDXwYQoWgdLDUX8O4ina3j0xSLl69wt4//jV6h2s4X6PVSX54OkRrxN0LVMIcBhEOhDq9gE8Wtufskn1SFcfphb8AKh3LLi2zvCA3NU4JXCMCU1qFs7OBJw5rPaw1pcLUuNOGefq9zo6XYOGxTwtTH3f82Gjt+c1NxoMhdVUzPBrQaifEUUQSxRS+JE1TpAyEvzFNWOt8KCD1gZyv6zrMqahrsrhFpCOcdayurFCUJUVVhkqVpi5wnnc66xYa/fpF10lVV4zHY0xd473FuRprK4RvjNMHg7M0KKGQIE3oL2odRV6EXPgYYY6IdBQMtYHxlQo8lJ43oPKe2pTs7Tzi1q1POBgckVc1D2/f4e233uJr3/wGOk75nd/+ddppwuFgzObqGi9cuwTKUwnJxvoqwzsPWN68wE9/+6/wK//4l9lYbSFUzP7+Nt/9/utcf/5FvvGtn0THKb51npbsoNMend6Io4MhL7x0jmefvc57773J2tIy3/2td3jz/Q/5T/7n/zOuXFzhL/3ET7E9NDiV8Ghg+M//n/+Uv/RTX2Hz6jWeeeYqH9x7xJevtildTqvT4/333uHiC6/iVNwIPzzCBbrIxRFeJ+SzgjhLSHVMp93mZ3/+L/NHb75B7Q3Ik8ZYAYY9Xq4ILxe4ymY6W/N1sBjmNmHwPItzT6BmzlIQp+gKueBpmzzWN+DRJJ9RVDXGNgIEJcPgJueOhSZSgJSNlG++0DjtuReNfzGHXATDThDZhbw3PPv4NRdvOXvb4vFU43zm2kUGR22m4zHD8RHWtRBCkKgIlUlarRQpwDhDURc4L5BaognFv7UzWOGx0pGbAp120EmLSAm6Sy3u375PXlm00HhvkQ0pO+cPQ8+QObnbADPeU5YFw8Ew1OmZEmur41EAJ4qOEM7KeezvQjjlqhps6PYtlWxALA0OjAGHRUYNYGSbpL35GIvplO17D7h39x6T2ZTa1rS7HX7rt36Tb37z66go5s72Pn/j57+NN5Yrly6Tz2bcuncXlcLbf/gdNjbP44Tin//+7zPLa3YPJ9y6c497dx7wcz/9LT6+c5vf+81f4YWXX2ZpeYWLW+usXNkiUpKjoxHvf3iLR/fucq4b8cknH/JL//Evcf/efX75n32Xz71wnWe2trh69Rp/86/+LP/F//v/w7Of+zKv397jteV1nn3pZX76hZexVjKZTChshXCeanSEWmpT+Qi8J/YQ6QCRzEzN3tEjIu3wVU0ad3juxg32RkOKWU5nxQf+tqEaJA0QyEJuRfjs5yDQHH09kb+5U49fXOSL+dnZEPLEuDn2zvOqENfgws4ZKAyxT4gkCGVJUxW02UIh/Jzwacyk0daKOb3yGON8Ws7ovcC7E1DKL2xW8zx5HvoHx/FjdkJI04j19RXarYTD/QOquiaJImIdkyaKSGum0ymz2SxoBiEYhAxQtNKaWIQxZ847hPdkcUQ7DWL3g8NDamfRhHHuUnkkLuSgqrnIY7ia5kMMZHboNA+29uAkkdQIbKBwrG2KkZvdynscAS53ziOUREcaHenASwkB82BGOo5ltS6MfheE2ZgHB4fcvn2Xo+GQynuKqkJIxXg85b//7/5b/q1//V+hlaX0ez2KIkdHito6Hj58RF0X/MRXv8Lq8hKjWcH3/uRHHKqS51/4AlsbG+STMeurK2hpeO+jD7j93g/5V//GX+XcWsZzN66RZh0e7h1x59491lZXsXnMxrogSTIuXLjM+XMXMGWOafV558EOv/Zbv8vucEzc7bCeJaxvXiJtdfnw44+4fu1ZsnYLKVpsP3pIK1JUrqJ2Hik0upG+OBGoBK81eVGg0MQqJWv3+PrXv3FM/M9D17lRHi/KBWM7G7z5459Pi+Yfl4t+qsCaM+P45p7zuKFYaGFSlRWm9ginUIAxObF2CGq8N9D0BQx86qJW98Rzns0jzyqg5uFsuOaTEP+sfM8uVq+IedH5j4nW1nVNpDW9bo8sSSnyHOED6W5lKFo+GgyZTnOC9DQIha3wpLFuvpwgRvDOY6qSWMLG2ipaaAZHAyQOiWOplZEmKUVRMc3L8OFIgaERGou5+NhjTE1VVeT5lF47RiqLlKE4y7swVAkHpiQUWaPwMm4aXAdQwhOG7yqtG8nffM5mja9NA6FLIiWQQlOUFXe3H3Jn5xEySREmlLQlcUpdVHz/D7/HX/zqF/kbv/DzOFNzYWsDcAgF3VaHyxefY6Xdo55OiL3hZ3/yc3xwcZmHg5ovf/VrmHzK/u4O4Hn55RdZW+5xcX2ZTAuG+4+o2l1iqZmNDxm0UzpZxsa5Djdv3qPISz73+dc4Gh4ycJr/5h/8Dxwc7NNutdje26coZnzzp3+anf1D7t57QCvrEemYjz/6gEuXthDO0M8iRpVF6SRweQ1/GCtNErdBCIwpqcopUZbSSdvoOAYb0E3vfFiX4mQzFWfyskVDelym9Tgw5SxpP1/4n2o92YTPc+mfI9RKmtowyQvKqsI6h1YW7R2RCoL/ubb1cbz604zxsedN4w4WHndKmriAqcyv68fmOeeV91JIsiwYT1WU1KYOvWInI44Oh01jrwY8keC8DcNgRcg1lJA4b7C+hkjTX1nC1IZykiON48LWCr/4Cz/L2toa7773AX/0gzfY2z/kWB3SvM4xMW1D+dlkOmVltRVaddoKU5ehXEpGSBlhZISLNB4JQuOcxzjTTIgGE9wj3odeM56A/EZKg5AoFeO84Gg44b33P+D7r79J6RxJp0M5nRBnrTCZenzEK9evkUURtz+5ycvP32A4OGJldZWH2w+Y5lMuXr6EtBVKevJ8zPjogCyCd17/E7SzfOFzn+fypYtsXb7M9994nY21VUiXiFsp7X6X2ta8+/4H3Ll3lzfeeZtf/MW/wfVrz+BNaG41zSeoNObOwzu0WprXXvoaX/nyF7l44RJ/+vqbvPvee1TWsLK+yer6Jtv373Pl8iVaqeJoeIjI2ngRY5TGN2CNBIQWdFoZus7ppIJU1zy6/QErm5doKYWr+sgI5pHcHEybS+8Ep6kU4LiI4KwxLh6LOt1FQGhumGcNCXeiyRVChIo4gizTUIKo8Sask16rRaqThpc8bSiL4oCzxvlZYM88NH9cSH6yls90R2hapj7ueKpxRnEchAY6CryUBJkmUIbSqrIsqeu66cWi0DoKBi1FGAsvgp5UEobc1s7glaLVbYfm09OcVEe8+uzzXL90EScc57fWef6Za8xmOaNZiVKK2pzsnN6HcDlJUqTUWJ820qoER6uZxB3jpMapUFw3rxe0VYlAEukIgabMKyYmB8I4+ihSRCoiSSTGWgajIz76+BZvvvUOdx88RGrJ1vlNjPNUtcU6GBwN2Nnd5Rd+4iusLvU5fHSfui4REiazMcurS/zw9dc5Gg7ot9pEOsWTE0UJmyspa50EMx0wGR6FwUsq5sVXvsCP3n6L1998n6984XO0DscsL/e4c3+XlfUtvvzNb1AaQ9rOSKOM3d09VjfXOLj5EffvfswzF1Z48fo5rp9fYzod8s2/8BU+vHWPD25+zAcffEArydh5+JCvfeVLaGnJspj7Bzu01i9S2TCR2jcLR6Co8gl7Dz5Crq1j05R7779PGmms9IhWH91fCiMBvaVBVk5yz0bPdqpRcwPgSKk46zjC+y5SEzRNsRS6AeYel3eKOd4wf52msLo2FbUrqesZ+TgHZ1BO4Z3E+3loCTSbiJDzZtfhNjhWXBOEDicjJsJtDS4y520fAw7NjbPJgucvG+Cvp7RCeHpYKzxtHRF5QdTAIlaHkKenoKxLirJgWtRNEyyLc4ZIRURN2ZUSYSKztQZva5TVaKmY5QW1g3avQ5TGfPjJJywv94ljzfJyl+XlLqO8pPYeL9VJEzEROiesrm2RZcsonTTtKELjaKSnFgItBbEIY+29h6o0VIWhKi1RQ8sIJ4lkGq5LaEbDKUU+IZ8V7B8e8uHNm7zx9tsMxxPSVsbm5hbFDJwv8ZVnNp7w8MF9hkcH9LKM2WDI2uoaK+vrVLYin02Q2tNrpeAdRW3xMkFGMdVkhKDiiy/doLu6xfBgl/VzlyjritRLPnfjWR722tRVzge3HtLrdDk6HPCNr3+T7lKfixcv88nNW/SXlxjkOR/e+oTbt28x2N3n23/tF5gMR/gKNJo0inj++hU++vBdvvqF1/C24JVXXsRay3vvvM/q2grtCOp8hmt1SWRM5FyQ7YkWURwT+320XcUWnlaRI8eHuOWYYvAQ6z3Z0hpKBKS39ILaC6KGnjiLrM4bZ8+N8NhzisAlm6ZgeY54KulBhC55itN5KAAy9Fb23iKbVCqM/aso8yl1balnY9xkgkLT1i10miESBUoedyM45S0DE/upjaOx0WM44zhfbVK6OTB2glGHDcr5RojYhN/IxsB/3JIxrVQg8n1Y5M47aBpKaadZXV0Fodje2Wc6LY7RrWBIJ8qNgJoFbkjFEUJJDodDkIKs3WJtbZ3N9bUwZdg71lZXWV1e4d6jg8B/Io7r/wC01mRZhlRhbos1Buss84JY730YWuMsVRXILWMcVW0QSmG8IFKKJI6x3pPnBY929zg4PGA8mrHzaJ9Pbn3Cg0cPmBVTjLMoA9ZWVFVJVdVMpiN2drYZDAY46+ivLHEwOMR7yEsL3rO9/YhOu81wMEXKBIvAeIeMY3SWkSRdPvqjdzl66wOee+YZyiLn4PCI5198hXIy48rly/zz73yHG9eu8yu/8qtkrYyf+dm/xIMHD7l24Sr9LOX17/4LhsMRX/ryl/nqF77EH333dxkNB1y9fAVTFaysrhKpsHm10zbOCryTfPzhTZb6PZRUrC4tMysOidKUKb7ZhgWOCCMyEH3KSiNabVrLK5iPoBwMiGKoS4coSnQaEam48WxBE2vwx9TY3Jss5oqfokt8s2D9vF9w+C6tMw3t0ZRunfFMx6/BSX2mE4GWUwhkZZANSCgjjdaKWIWO+arR8YpTr9JwtE/wanLRadJkW96BbfLOeZQwf4Kf90Gyx5z9cQSAheTHME5rDEZKKhHCBC1kUxXgj3OJdrtNp1NQlvWp8NMY2xRXN5uMlOAE7W6H2hgOj46w3nE0HPDJvTuc31yjpVMirbh6qcXO7gE3794jr0aAZK6DnYsfut0u3ntmsxxjzan4/oQftaFTvQsNxYQIo+tX19bp95bQkaaoK/YPDpje+oQPb37C3bsP2N7eYTgcYmyN8TVCQCtNiHTEZDJl/2Cfw8M9JpMx1hqEd6xsrpGPhwwGI5Z3j3CuZnA0IlKKb3z9G6yurVHZmtLU1DWIqMOb73/IzQd7JEqStTI+fO9tNrfOc+/uLY6mJctlyXA04ta9u3z7L/8cdVVysLfP5QuX+PjDDxkf7vPspQt0Xnweg+I7v/s73L9/m1vCIGXMcq9Hd3mFGHj08AEffPAh3/zWT2OMpd3OmEyGtLKUWT7h/qN9+mqJZCkGUyMaaZtxDitqsthRTR9hljzLW4qjBx+zEp0j6bSII4+WocJDeA/ipI0lnDZI70/afS7meSe5mm2+7VC3eky3LAzpXTzm6h1//D4nlI2UkrooIS8QVYVwBkmEFC6IVmwThi8aEo2HFKFkjPl5NUYGJ2XX89XuTTjPUHp4Jt88ZfZzjoljTvnY0h9zfCYgVAmJaEbMC6XQQoUFKcL9xhjSJEEpTW3Kk451+GYcu0BIifOOOEnYPLdFbQ2T6QyEpKgq7t69h/38F8iSjFYrY31zncp63vrgQ4bTGWV1qg6HLMvo9npYY8FHCGRA3ZodMHhUg3OBd42ams5uL6bd7tDvL6OiKBRgANOi4M133uWHb/6Io6NGOwxNGZAnSzOSuIsxjuFwxMH+AZPppCmT82itWF5dId1c441/9KvUNZw/v84rr7zKd373N/nGN36Ksp5QOU9pLHHW53vf/WN2H+2zdekaL964wmywT295hdJLJoWl1V/jYP+Q8WjMdDZjks8YD4a8+NwLLPVX2NnbpZ4MWe5ssL+3zerFKyyvrRG123zw/nt8/8338XVB/wc/5K/89b/G/e1dzl/Y4l/8we/xb//tv8U//bV/wqUL51haSpgWI3KvWM6WsF6QSoH2Fu0N2BqhBUymmMkjJqbCV5bd3UNaK+u0vCJNusi4HdYaLsz3FLqJ3D7NVcJp0fdJiBpC0uA0w/CoY7qFYHhnKQqlVBDIh6URDLvhLq21VEWOrGqUsU2IGVRBrq4RLhjI41pgnlI0HeefjTE6f5yH+obia6LZ43z1ZNLYWeMTx89r9pwnHk+X79UO62tqD0Jr/v+s/fmTLcl13wl+3D22u+bNPfPte21ALShUASBIkQQpNtVkkyP1YpT1zJh+mDEbG+tfu/+LMRsbm/lFrekembqHbIkiJYpDURTFBSRAEFsBKNTyqupVvX3L/a6x+DI/eMTd8ma+B0hh9l5m3hs3boS7Hz/nfM8531MIi0X760tbxk/9jhGHMUVmQHjiIlU67xZHpBShVDSbdTbXN3jw+DGjLGcpCnnp+nUC6bVss91CSEGt1eLy1atsb29z/9EjClENgH+wKIhpxHXqSZ2kFvu0QV2Q5xmDwYgi8+b3mbObbGxsEEVxaed7pM84ODg84snTp/zo3R/xve99j3feeYder0ehM4z1oZQq46nQBUfdIw4O9znqdhn0BziqdncSIRSPdnZ58cwKQvdpLbVp1xOyo306a9v84J33+Nm/8xZZPsKIBk93h3z3RzeRgeXcmU3u3LnDpc1lgqU6QdLi04cHGCc52tvnwuYqN16+wYef3UfImDhpoo1moIc4ZyCI6Y0OGNx9wM7TPbrDHl/+8tuIwtA9OuLmpx/znR/+kAvntjj44EcURrO32+OFF6+x1LY0a/DkcZd65yJp4YilxiiJFsIXFFDQOxqQ3TukUTNk9Q0SvcRGe0C93fBt/oxFhT5nWZW1sBaHKyulRYXienuXQImSGmXKJB0vZu+bTZJKJgteyDkmhFLYPDLr14cVsmQokJjCMOz1CYX1TY2dop+NGOkUJ8oaXetdImutBwqnrj1NjOeOCVH1ZoXkirn3xAQzmhPu5z1OZ0JwAltYCpvjtCaMAqzyg6qrCnH8LhKHEUWoSYsCsMRxTC2JPXgHNBp1VjstGvUGu4ddtLWcbS3z67/0NWQoqNdjVKiwDrR1rG9u8dprr/Hj998nzYbjhxRIWs02UoY4J3ny9Am7ezscHO4yGPhuZI16hxdfeJlzZy6S1OtkRU6VznVw1OPmxx/zne99j7/91t9y9+7dMWu9MaaEGD3aXPWW7BU5vX53xkeaBMhBhJIPP/yIVzbf4PzWGp/e/pSV5nWWGk1uvPwG/9tv/2tWt+9y5sIWS+1lfud3foeVzjIXLm4TxSFrjZjtpZhi1MeKgNwpwmSZvQePOLfZ4sULW1y9eoWPbj2gt7/PP/2f/jEvv/k6l8+dxwjFyvoGDx8/5uzZdYp7fWoRnDt7gf2DI0Qt5p0ffI/Pv/jfEEcx57bWsMMR6eEukRRYIo7yGsvNVe86OJ+JZUSAAd+ha5Ay6vYQzYDWcsLOrYeommBotA9XqICs8AkZWC+AFuPrYadY7YSrrCnGmsfjQB5h8dw7JbA3tZz9OWXJ2BikcWMmAqgaBkkv9Cgfd7WGbDQE6QjjGm6kGY4GaKtxpWatkha8CzYrgeMy78oXHk++ZD4DeGK2H4+ZVkjw9NqZ95cXHc8wa8tBMgIXgC1ZDRBVv0MN+FKjKDbEOkQ7g8V3GKvVaigpCKQkUJI4rmEMPHm8gy40URzSWV5ia3uDNBuWScfVLgv1ep2lVpujrk9ysA6CQNFaXkIEChkH3P/0MZ98eoswCljuLNNutQmCkF7a5/7DxyXfT452loePHvLBhx/yjW9+g7v37qJ1MRMIFnJC7zmf3FwN6DTdf5VXKaXk/Q9v8l//8pdYXl6hc2GbW7c/ohFfIIhXef3Nt/nLb3yHV45SwuAB2fCQt9/8edIs4+DoiBcunedw/wkHO3uMck1qBO99+132nuzyq7/4X1H0DzHBkAvrNR6NNJtLdd7//ru8/70f0ajX+OrPfoW3f+aL3L71CefObrDcafHpZ5/QH2V88NFNOq02n938hPOb5/iz//AXvHhui1ogEaLJwK0QbW1RiICwgvelGBc5S+DKhfPwlTfZu/8udz75W9ZXVlm7+BJ5bROjWtRbHaIwxFjjuQHK2kqBz5G21vqcXUCVpYFeCMsc1HF8dOLJTccUJyhq+blqY8TMJguUHbOlFBihgYJcDykcZMbQHQxJdVY2XzKlmTxb8rVIwc0nwFsz+970z3mBO+3vRfHd6eP0kjHfk6+M7VSxKl+hoY3DGO+XCcS4s1hhFFIl1JKEMIpQVCVfDoFkf++Qo6Me1jhqSUKRZzgMyysdhv2hpxKx1rdjODqi0WyixA7a+brKRrNJGEfcfnCP1lILgpCzFy4RBBGtZpMwUgyGPW7eep+/+qu/Ym9/n7zwZun9hw89uuq8/xGGCvA0Gz5BX4/96HF1/oLBnEYfwY/B9398k1sPdkldALlmeanNcqtBKgw3XrjEkyeP+OjmbbLsiF//jV/m45u3aTRWWGou8d57N1mqRxgR8clnn/HqG2/y67/5eX73d38HJzSj/hGFhcPuCJeP+Dtf/iLv3XrEu++/xxd+7iukRYYuCgSC9dVNOksr3P7kLh9+/BEPHt3l733tF7h+botv//DHvPzyDZ50D5BxjBkmRLUmIghKX1Fipc/GcqLM+MGiQsG7O4+JC8nZzirrG2uYuE4vD9HCsiFD3xColC8f85PgNNWuKqokAeFpPSZayiHVdOA/mBnf6YC+dXoc6J+gnd7Ssc4zF1QF2tamjLIu3f4B/TT1glnkCCVotuokSUgQeDxkJpwzs/7FOPFhJqZagVBTf0+vk0UCOS/E86//xMKZ50WZce8pSJTy8UZtLIU2GONNCmM941gYKhIXImTk6TGdA+ETFoQUDEYpD57ucrh/gHOW8xfOcfbcGaLIw9tFUWDxaYMff/wJT58+YWV5mVrtEXlvQBgEXLx0id5gwPd/+AO0NqjI8wblpiAtjsjzEZ/dvsm7P3qHnYdPPIJYpovFSURSlxgtwXlE16cBjigKL5Se3X12cuaFcxrICIIAYy13Hu3yB//+6/zil7/EN/7mb/nqq5f54z/8V+ikw7XPvclLL1zzTPTNLT7+7CZ/+70fcWb7Cq+/8RqdTov+0SE/evcDdJ7T7fY5HN4hswWPdp6wtVTH5sZzGmlYWVni7/8Xr9FLU157/TXiJOaf/87v8n/8P/y37B91KTKLMY7RcMCVS+e5cG6DIu9y/uw2eVKjff4CP7z1kKC+ihMhsUtRyhc1eBZ/i3Sy8v7Y6R3QPHuFLX2d3U/ukMkeRVrn9s4+V1/4PIUFkxdEqkyfw/mKIDdxe6owSYWmCjlbzTElEuOxnUZ2q4SE6cU/3TLDaYeapLuDMDgKDo722D08Yv/okKzIqcchjUbN++tTMdNFQjUej/GaqN6XVEwMx48pdHZmAz9eYjYNli06Tu+Vko3KARDjEiovnGbskxntMzGq7wiD0PsGRpMXHsl0Aob9EYPBiL29fYo0R/kKa5RUSCtIh0OG+YCk1uLRzlO+/8N3GA3L9umBn4BakhApxdMHj1ldWWXQHxLVYkZZQZrn7B3sce/ubW7f+oTu4S5OGAIZUqs1WWq1cVhGoy6DwZA8cyUHkp6wqDl7LDBe/T6fiD290/oFZPjDP/lz1peWuXHhDBtrG2yt/SKH/T5PnuwwGlmGaZ9bdx+RZj1+4zf+Hj/6/g/Ye3Sb7fOXOXPuHKEwhMIhkybffuddlA34+je+y5Xz61w5f5aiGJJECWEkyOyIay/e4A//+D+w1q7zW7/1v+Pxw9sc9Hp88P77PLr/kHwwoL21yv7hgAMpqG+c5cUXPs+PPrvL0tYFRpnx4QLr+3wGUpSNhEvBsAbtFPWVs9SKjIOb71E0WwzqaxzmgoHNeLp3wKWiIHYQECHQaJehnSFwaoxaytADZ1WXsWmZnF2fs0kGs4u6Qj+nEE9Xir/yhQuBsEgh0blEG8GDR3s8fbJHno9Y7bTYOrtNPWlSWIt2GqEXC5R3ZadY9Uo4uEJ8p1HY2fVwmh854dmdrLefkvFda4022guQrAL6fjFmeU6WZZ5eMgxnms3KKnisC7QpIBfsHezT74/o9QblA8Kde/d598ObvP7q5xiMhjx6usPKuuL23fu89/77rC0vsbrcoVZLEKKLs5Ynjx7TanXY3t5GAFm/x2HvkE/v3Ob+w3scHhz45j8AKkCKgDy37OwekWc5WZ5idIG1+hjMPy2Y8z7PtAlS+Z0V273RGmENh90e//Pv/i6/9V/+JufkJdYa6xTdnPZKi8Nen3uffUQQCF44t8mVtRqXfvENPrh5h2/91X/gyvWX2F5fRucZ3/nO37J69jzxUsxHNz/k3vc/QrU2WVteo7ZUo7m8hHOO9959j9u373Dj0ja//9t36Kyts9vtceX6NTa2z2Od4PqLL1EgCesNmq0lCBPaSyt0dw590B5BFESldSMx1hCWTZKkkEihUKpBquok586RdXs86eV0B336vR5PxUOMTTE2RWsIpMOX5kqUVePEEF8nWWZmMZ+it9ichOO+Z6VBJ8kurgrAlPfsKMp4ZlJr8Zu/+V/zn//qb9JstWi120RJTGe5Qxgk5Jku+4RWZvWUcFZ8tON7OB4PnT8WhYxm3huTcFcEAlClNy46TtecJauBc3m5GAMfmC40gfLJ8PMMalWCbxiGWOcYZin9fp9+v++zeazG4JAKPrl9h9//N3/EYW/Iylqb+493ufN4j+9853s8evyE5XaTKAqJ47CkQBnS7Qq2trbROuejjz/kcOcJ3f4Re90DsjxFWkukFFtr61zYWqfV7hCGEUKFOAKe7uzxzW9/m/1+dixmNs/zMq8x5wd7encPpcQJwW5/wP/rf/ltPnnwiP/qV/9zBHU++PH7dLu7XLt8ls+9cJl65Dh4/Bn1OObCZofD/ojvfu/bDPtdXn35BZIkopEoHtx7wGsvv8IH793iX//en7C82qbVlrzxuRc4d+4S62c2cJHixguXsTpn6/wVVgcDmktLDNOCqN5iGNSJai2CpIHDYvLCcxA/eIIgJAyDcUKJUsrnQpdtCPxzQmENQ23InSSPGxR9S/+gj8lG5EtDev0DotYSVXheCFmGIZQPV5TXqSpAqm5i84nl1cJe9Ptphyt9TUptjwOlQtqtVb76c1/DOjumu3R4F63KAhp31XalMM58/zQYddxXfJZpOlN14qY1rkOq6fSExcepwrmzf+g7JktFFIZIVXZWVgHgzdrphT3dhdkY46n9jfHZNtbgsTSfEG+sIHfwaG+fv/jmt2i2a0RRRLff496Dh757trOEoaLdbpQD4XfKx08ecnBwiDUGnaY+j7VIKYxGZxmyXueVVz7H3/+7X+Patcs0Wg2CJEbIkA9u3uLJzmP2f/TjqUmYDP5JLcjHi2BqYkzZjNU5R+7ANzXx53988x733xzw5S+9zUGu+dVXf4Pb77/DX3/rb/jql75IbnwpmlKCi5cu8Z33PiHLUx4/vIdzllsf/ZiLFy+x8+AOVy9t0KgJ1jY2SGo1gnqdQ23Io5Cl82fYM5pmo81HO4cgFQOVktR957RcSELncNogA3Ba00gaNGp1hsNijKJKMVkstixacMIns2sKjM7R/RSrfa8RFUmkCslyT84WBBFShN4UNAarXEni5VWEEJ6+0jkQ1o5DKfMLvlrI1h1PDJgHXcb/qICocn4sOCcJZIyTZce3Mh4qqTiCyo152rym4sn12n7W9D6u4avN+Vj52tyaKV+Z+ukmf/20gNDB4SFSKt8Svl6nVqsRxzEyEDNcQdaAVH6CxhkdjrJd+xFZXqACVXLNlgIsQaqAwkBaaFbiBGMKBsMRqyurxIGPZy41mqytrhKGElM4BIZRv0dfH3luVGPJTYHB+MZFznHQ6/Ev/+Df8hd/9g1a7SZLyy3aKx1q9YTP7jzgo09uTVC/BQO0aMAWmSnTr1mfw4KyEmUFw8MRWe4YGag1l1lZ3WLj536Rl157hYcP7lHIhKPeiKU4ZG845Od/+WvobMBGp4HvKBFTOEU/62PQbN24QFYItA0RYUjP5axdvEhWaIzOGSBQSRslJWESY6XCCZ876pxBmByrgjL303H54mU+vPkxQSh9f07lW0sEYViajT4/2hcVFJg8JbGavDdgeNgnzTNyPSIUAaNRjrUlp6steV6n0EzHbAqfcJ7RovK7jPHukAqU19ilvwezHtzMHIhJ8yJXjr+3ABRSifEHnQMryjXnmBB3jQWwjLGW1x9r9LKpUfUFs27xYn/zRHN2fFQpjRMNOtbcC47TE9/jmCLP0YWGUbkgjaWQqmyl7h18rQ2x8mVYxhpcGS/cPTii2xviW6AFUDYLregGrdFI4VhZ7rCxusyTR/fZf/KEeqPJ5vI6K0vLNOtt1lZTIqVYWl7lV3/lV1hfW0XrgiLLSdMReZFjjEaq0Ff4y7DM5fJhEms0vX6fbq9Lu9kkVAFKlZU0cwWvi2r2pgVxWqCnQYDAWpxwGKHQCg6HXY72nqCPDllpdPj01h0+//p1unmf1tY5tIZhWmCFY31NoJQoQwqAAGMcMgehl9DOkWuDSTPfNkEFRC5BOUsURbi4gUMQRb6FXxAERFGMxBctIEJy6U1W/OWpRb41YqFztAiJw9iz4lUbpwBZskJkmcYUBVFgyXSfbveQw4MucRLRaEb0+gO0syiFB7SUQEiHtIvQWGYqMZxzyOC4W3H88HbXonw3KQShP+U48iqETyecmsexIJUEcdXr1QYiy0LzGRN36vv8Gmb2e6r3piyu49p2Wqj9VX9qzdlsNsnznLRsAmN03ycPhBFRGPnW3dI39tHWYI2/oaLIGQwGHB4eorUeD7o3ASvWdItUhqVGja21ZZqNOoNmkyRWFMMexajOoN9nMBxRr9dxTvDmm1/kf/jv/wc2NtYx2u/mWb9fbhK+HwpIojj2+bxSoIIA6yzD/oDBYEC/3+d3/sW/4P/5j/9Her3+MbDnNP/y9EP53b7cibO8z+27NznsXmepscLjJ495Xb1EoGLC0CAVqCBG66Js9OpKf8/7Zk46jBRELqQwljqCRr1OoXVZS+rGqYiupLwIwgigBHE8H1MYeDY5VbIbWAHKGuIwYKmR0EtTQhl4oRY+n1UBgfJ+o0J46tGiQGmLQSGVopYkxJECnYPJCQNBoCTKCazwQlQxG56kURb5bScv1krYFoc95pGaSVKBKIGXRZYPjJNyEcggGOPBsDAf4T/Z8R+dIWStJVCKWq3GaDhEF5pB0ScPI+JazdfCWUkYhjgpyIocnOPw6MD7mWXfzir7Q1sHoqRgdJbVzipXLp2nHkpGgwHtdptf/NmfIVKK7a0tljtLrK6uc+vBHZqNBtYYQCJF5EuHIoVciQniEKXCMqLmqwmEg7AEHkCRrCzTWe6gtebXfu3X+OM/+wveeeedhazbJ2nJ+fenhnrqnzeytDY8enyPu/dusbbc480vfhGBIInrZLnBYZBK+Aa0whekS1fWv0qBk2DCyq/xSSACQWEMubEY55FPhMK5soKi7HpmwbsjQYCSHiPwBfHegJNoAie5cek87978GKs1FomIIq89ne8nonDYoiAOFBvr66QHlvWtOkl7g2G3S/9oj34xYNQ7AFOggtJMKzcbj1CePI7TC/W0zXEyxos3TpzDh9yPC7jAt2lYNL8eRfbavbryiWb0/J2cIlcnJSQs8llPO04Vzr29Pd+zEspgrIGyx722ljRNyyp1SVAmKBRFQZaNMMYwbfoZ7WOhTviEYAmcPbPF9voqu08f8+mDByS1hNdffoEvvvk6r7z8Mu2lZWQQIRsxcRKTpiPflsD5TBJV9jJxiCql0z9wCQ6YMhMIvDZBglWSpdUVlpc7pw7MomNRdlA5zIyL+UokwVnHw4eP+PDmR6wv7/Irv/orCKkIw4QoKnB55juXSYXRmkAFhEIinEU5h5MOKX0doCwbQflkfEdhne8O7pxPBpeypJP0dCF+HEoEViqUVKUvWIYLMEjrGSyatRoZgigIfJfnskGtLMMfSkqEAhMoljrLjFJD7kbo0Ygk9NxM2WiA1QWE1s8vlExzJ2ufRajsaRkzi8zH6WOakGveDamAnulGS9WseevSg422QpaYRCQX38usWTsf9150z4usr/+oJASdZphqQY4ddcD5tvMzNytmA7XzO2El2IKK2d0DAPVmHbcraLbbRHFMoQ0PHz6kUYtYXlkmqtV4dP8OwjnSkS6DxxlCKpxVSOtNZJ/vUdkpDoQbg0/OgZIegbTGIFBEYQ3fEtyUXLtQ7ZuLUq3mj9kdERwSyv4aZcE7h90Rt24/IkshihsEUYAe9qnVayAU2hhsYHzRb4kgKkA5n9Jm8A2TpJA+DonASotyEIqAMi0Vh/Td0YwrEdZyHISEsuEu4P0ywAXSWzDGIK3fWIzTCOkTSGSZaukHzKJMSloYLDVyk2GyPjrL0IUmjGKKYUqejQjr2hfoW4FwYlysPAO2UC1sMbPA58d0wYjjnIIxyFShNJ7JwG9ItrQyXBnO8QITUAI/ZQvHqZnz+UTlGlBSjaVuiidv/AyT9TFFpVmiwIjx7ZzoGtnJ5cC5sirGjudl/nimWTvWFrOYc3mTE0CkyqSQYrGJUtU+ThuQUgV0llfZ3d1FBBGXL1+hEQVkqebxo10Ojw6pN+scHOxS5Jo80xRFUaZn+VaCbuqJHW5She78srV2Njna+2ERmxtbqJIqEzGdsjf+beEAz/8txOyS89fwY5FrzVGvz+df2UCqCOc8MdnKygpKGnynKq+ZFKW2QoDzlOgSQRAG4yGXQeAnrFSPCk/rYREoEeBCWeY9G2/uCt+AmNL6UUKM2Syc8CTP7VaLvadPSJK4NJO9tpRCjg1Jh8LqgqK/B2lGpDOc0xDFOAyD4Yg8z6iP83HFeMEusgGdq8Ri8bieNN7Va9PCbKcAnenPTG+w0k2snnkzszpPIkqeZH+Y8bMsAqBKDcxk5Unntxs3rkud9St9LFZMXc6bFqdtR6fXc5YXnqncmBK8+fhOFfeZjxGOy7HmbqVRb3Lj+kvEUY0f/eAddJoxLHz3sLX1hDNnzrG63qEouXBNxXgw1TxlvMOVi8KVJi3jnp7CE5ONyaQEUVLjxgsvkCQJo3TIlIHz3Mfsc8+MGtVm5Zwjz3Jee/XVcS+Per3OcDgkDEswSJaF4mVVvJCy9EG9xnem9JtFlYtaBtNNufqNKQPqZf1guQG50rxW0pdReTdwwvsLfv7CMMRojQqqPFULTnnk2HmfNtXQPzxE73xGdrBDrb1MFLcxYYMs7TMcDDBZgTD+GbxGL7Ng5nGbUoNOj9lpwnmSgE7Pw7NMYTmnLKbdrUVJEEKUoJb0SRXT6985vDshfL0yzif1m3Jshe/KchzHACb05GUzJilwJ3LvPUM45x8IZhN45yvI5zVm9eBVap/fTqstFbpHA3ThOHfmIjtPHjMYDrj9dI+HD5/yV9/5Lme2Vnj55Ws44RMesiynKHJPgyjs2LepvmeyCZTcpioiCKYCzuV9xXGNL3zhC6ysrHD/wWCs/Z/nWLwQXGlKTVLAnPPWQrfXZevsWfK8oNGMaTSa7OzsELfrKGXGBeBO+IJzEUwEQxhRNmYtN7yqCavz9ZLWOqTyrS/c2K7y41stFlfC9ZUJ5jdsCyUQEoYezTZao8rSPiV8tYawFikgCgOSWg1Tq2GGISqURIHDyYBMK3q9PsNuD4zBSa/1HVOZpPNma/Vjyl87ad1V50y3QFhE2HySvzf9/WPBm8pmm/589T0AFo319IFlBwI7GV4qkM5gtUbnBUYbwjAkriczfTmra1rnMGOXg9IXcgTBya7TM4WzetCTEnSPtdc+JU44PX4OQZb5HpqtZovV9Q12PvmQH374IXsHfYSEj+98yoeffMiN65cQwlfJpGlOoQ1KeY3okbZSMG2ZuM44g9NPBrP3bq1lqd2m0+lw7/5dxqulLLSuxm7+OG2HPp7I4HfWJKmRJAlhGIw7LptycXiCscrAE2VsTXozdMpMEpXmE3J8f6qKidqK35WJz1v6Yw48ilkeFe0HDk+1ETjCKCQIQ7K8KE3aEj2WJcuDM7giw1lHrbPBwVGPg91D2isSGZhxpVKeZUhXjXqpUSYjO6sB7fiGSwusNBBL18jh0/Cs8T7iBMDx7lXVZXw81lSG0+RFCVitybIRVlsqNktRgj9GF2RZSpHnRFGEUJIsy8nz3I+T0T75wnihC8MQbS1FnnvCMWexWtPr9spwY0qr2WTrwnnWNjawtkEQet4q52EICmPQUwwPDlDG0lm4qp7DrF2kDedNiTFt5VQe6vTOZqcWkEfzfKggtxptPaK6sXmGO/fvYfPMM5VZRYikqQLishoGBM4qcL4hEVZ78875XcyYCXuBUgojdNkfUo5NWr+QUrCOrfV13oPxApiFAJ7/qDTmWDG4yb/19U2WVloILKGIkSLACoW2llYck2mDsQIjhdc4DoR2ZW6qKxdc6VvbEk4T1gNIym9CDkome689pRRYy3hDnBjtHoyzLsA5jdYjwriGVAF7O0esd9YJohClfMBfOAnGEkhwrqDIfWuKo4NDrIHa0jL9UU6/22U47GF05uOrIqTqa175hda5kslA4IzPzy50Cs7gjPEAVAlO5XlGWjK0O3y4yc/hhGhaBcGEykQIgjAkCCOiyDfbEk5ztL/D7Y8/8vfabCFUzCjNGPQOcTqjf7hHkadsnztHvd1GWxgOR5hcY02BMwbjHEEYUm80SWp+rIoi9e6GFDSadZZUm35vgLUO4zTWFl6AtSjnT5XYAOVm68nznPVZbbC5cF09t1n7rMU5bXrMl1dVAzjeRf2H2DvY57PPPmPplc9x5sw21w+v8uPNdY66d7BKUItCLp4/S7NR94isMfT7A7S2SOWT6MU48aEY34uSnsqzMIWfSKUIw7BMeIYojFhbXeUf/tY/5Ic/+iE7e099wrSoeGN+UvGsxmHyWecccRzT6XRYarex2mCF9uavlCVoI1DSx3+1sUhX3oAqteaU32adK9PcQAVi3KavYojznDhTQjkeaMbQi6HcHEuBlXhmgqV6k2HXC0SShKgyKVuVPD+jPCPTBTYbYaVERDF9YzFZwe7ODh989DGd9Q2uvvAiNecQIsAY3y6x3+t7hgSl/BxqQ5Zl9HqHOFegi4xWs0Gz3sQax7C7y8H+DnmhQUrq9SZBGFIUmqP9PV8BJCDLs7LHKly8coXVzXPUmkvE9Tp5EDDsd9l7+ph6HLK5uUWrvYRQIVmW0e+1OdjfI23VGGU5qpYQJDUatTrLq5J+t0e/d+TZ4quU0yhiZWuLdtuzPjjrW9yHYYwcZ6I5lPIFIkJIjBMlKYHX3IUu6PWOGPSP0EVKlg3Aai5evbZwPT2XWTtZfCc73/OatDpmTOI537TfH3BwcEBRaGx/CFbz2isvMBgMeLR7yJnNNS5euIANGQ9AlqXkeYaxhrxwhMKbHIu0edUbNAgCGo2GzwuWEqUU7Xabz33uc7z26mv86Z//+7EwOFe5A27mGU6LYy0CFCrzfmtri+WlDi7LcMpTaMhAcdA9pFWrTzzd0jf3LQLmTeQqPjntRwGyTNKufLEyO8lbAiAlODHNQG7RiPE8gN/JlzsdHjzeJS+Ksg+pJ+X2fq/DDQtEZjh8vM/tTz7i7r0HBLU2S6vrdHWKUBEHR10eP3rM1pmAJK5jCs2o3ycdDonjiFoY4pyjnw/QRYqSnvl8ealNq9UkkCFxmOA2VlgbnqXb69PudGgvdUAoBoMBO48eYo0hjiOP2ltHFMUsLy/RrNWoNVs46auhkiigHkdIIYmbHcIk8dlPiUbVmoStFUzhgbAwiYhK6lOsJR+OGI6GvldMFKHCiCCICKIIWTbetdbXA1vr0VvnPD6gvM+ANo7CaIx1ZFlOlmd0D4/46OYH3L37KQLN0eEOeTbil3/tHyyUqWfQlExM05P8yeq9apHOI2wnxrCE96cGgwEHhwc0Wp5v6PqVC7SbDf78r7/FUrPOynKHoc19V7AgIAzCEmTSGMM4RWz6fqtKkWmhLQrPFxQEnsFNBop6vc5bb7/Ft7//bY66h14DuVk/ZxqCFyVQMn1U76kykyqKIm7cuMHFixdZX1/ny196m0gptJtodRUG3vcotWKV1lgldIhS7VXXDVRIlhWl7ynHoR/fft3HFKvCYGNM2QbPJx5I5cMmQBnTnFA+2lKQHzx9THc0IC1yRllGpCKkVJ7a0lj2jw7p9vq4MKSzuc3QBXTWz3Lu8lWWtzfpbKzTWOrQ6qyQNNo0Gy2Eg2Z7Ga0NURyjlPTs+9ZinEG7AueKshu5N2slCiMUtQ6sIgnCyANeQtLowMaZS2M0u5rXPM9JB31U4SlusiwFFaLiBkv11tgSEhgwBa4ocFpTq0XES0vEcQxSYDAe+XaWIJTUog5h0vB+vpT4WLIXSG1znLHjeLLBeZfDWrT1vWoLrekNehRaMxgOeP/997j32W2ePn4EzhJHikG/y2DQP1H+niuU8ryvV8c0jccigQWPvdhC8+jBQw4vXyGMFN1uD6VTrl+5Qr8/4LB7yOrGGnGak2cFujjkk08+Zn1zxWsSqTCyIBOjcf1hoT0XkBM+cSIMI9+w10Kapd5BN5a0zGI6s7XN9WvX+d473/NgTBmnspXTSLn5CBBKlg7+hFtoZXWVCxcucP3adV5//Q3efvtLbG9vk8RRSWoWYFIPcDks1miUEyRhjLHO+8+VgAtJoBg/C2aiNaUUvjmUFASBRJbhIVtYnPHE2RXYoAJVdvL2DaW86SuQriyEHycH+E3joHeEkdYzoYsA6ZTvtSoEKgq4+tqr5MOL9A72iMOIzsoqUdJChjFCBDgp6I9G7B8ckhfG05vGtbKxlW8SZW3JIy8EMgiJZORjlMYQKIE1hnQ4wpjcU1RKhyh8L1VvJEiCqOIXwoNaRvvGU0FAENQxzhEpicWbDb7vicOkAwaHu4x6h5gip95aorW8hjQKp1UZkcqx2ndIN3mKsQE1QhCyfCZVZmIBeL8SW4a3sBSZptfvkekR1mr293a49cknpFlKmme89/4HDLtHWONroQfOd8trNBonytFzmbWnmbOVEI4XMcfN2skCLxd5KZwYy2g4RErFwcEe9+8/JBKwtXWGl1+8we7+UzorqwyfHpDnmijMODrYYefRQ4oio8gzQuGr+MMo4KB7yO7eLnmRYa1BCsfa2jrnz12i1VrCOYkQAVhLkXvu0muXL/Ebv/7rDIdDbn78kS93qvhlhBcABGX2iKRRb7CxuUmr1eLqlSt89Wd/lhs3bnD2zBnqtTphEJelSQ4lLdJpHAYjLBovXImQiFodYxxhFEFWEIXKN1IK/MqbMZWlwGHAmbLhLz61zyqKwpLnFu2cT2oPBCoQOOxYqC1lRpAVOOlAGjyTl0IYRTocgrQoKVFG4ApBIRxBJAhVwMUzV8lGQ9LtFKUUSZJ4wuZcowvvalitqUUhcSRxLkdbhZSe7lQIH2M1ZcIAxoLxQIkiQOeekyovWTdUmcA/donK8JTRhklz5YmFFKgQZIDCEUk1jmmL0rIwYY0waVJf6iOdIYwSrAiQssQhSu1qUGhAJnUC6fO3i8KgTcl2If04IpwH6JxPEcXBaDjk6dOnPHx8l6IY0Tvc5dbHH5GlOf00Y3f/AOMMcRjRSGqcP3uOCxcvcPnG1Z9eOJ8VBK6EcT5ONC2swLi63sG4wNcKOBr0ebDzmOGwy2H3kFAIdg8OuXTxPHGrSa3Zpn/3CZk1mHzE97//bXpHT+kf7aF1Rq22RK1eRwWK3qDH/uF+KUwSkxqCIGR76wztpWXCMEYISS2pEyUJ2lqGoxE2zzm/vcXezhN2D/bH6KYQglqjRq1eoxYn/MLf+SV+8zf+PhvrG4RhSLPZpF6vlQn+2oeYy7EwRU6qUxo1nxI3HXIKQp+kb62hkUTEyptnvldoUCLOugxpyPH4GeEXpwo8DWVmLMNCkxsLWAIliMr4pXXeR61K9Cj9aedcCQL7LB4hxYQF0Vq0sySBIogkSgpi6a2FIIppRLEX4ECRZTnCOOIgoN6sgxBESYwMSk5XGWJcgDMFzhaevDkbMex3GQ4GLHU6xPUmBQEWhYxrtFptz+DnKvqREsgqi9qlm8SyKzelHFR0ier6Mrdy3Ql8GiGgREhQX0aWaZ0+ndSOQ0dGqLILe+xpXwcD0uGOp38Vvr1lnpcCGiYEkTfVszRlNBzSPTzk6ZPH7D65zeHeE7JBj8O9PfYODhjlFqsCljbWee3zr/LKi69w4ex5mu0OtWbzpxPOebO0YkubzwKafr9aSDN+WuW7lsnYlGCDcZbdw32+94MfEISWZjPByYDdoyMuyCt0VjcxIuTh06dkpiC3gu+9+0Nu3/0MU4xKRvKwjCEVFEVBoXNyrYnixCeI54U3r/BE0VJKlltLJGHiF3iWecjeOfq9LsJZamHIxuYGr37+VV577VUuX7rExuYmF85fprO06pkFEWNcxdoQYzz0LzD0jvpIYYkj6RfbTGqf8MCDkBRlg516FPmFJwRSKE96bCEMqswoM0bBVdm8KStgkGn6eYEDamFAIMvMIuvjgUrIsgi8XKn4kjGPeJWNqRysLK8gh4PSH7QUlATQBB5BFhIZhATKJxe4Mp4spCSMvPZxQG48Q6BxDme112YChNOlwFVMmcKbinlOblKECghEHW18+Y1wvmiiuq5HQm0Ze/QsiaPRyIM1ypfHIQN0YSmKzK9JO0l4N0YjpfJUrUoSh4pQlmNgszHFq9EGa6HINYf7+3QPdhEqIIx8u5HDbo+jbpdhAQhFFAX0uke+b063i9Y5NuvjtI/FJ/UGV9a3Wd86x9aFi5y7eJ6t9U2aSYNa4vOrVXxCF6PnEc7pRTX/2iIUcxFlw/ga/kP+s8KHB7qDATdvfUJ7KeHSxbNYaXi6s8vdBw+5dOUyjXqLXr/vfScneXrQZfeoh2cmNwjteWqM1b40SNgSABlQURjifAGwK/ui93r9MnGhDGZbX6YFgtXlZX7la1/jZ3/2Z3nttVfZ3NwijiMf9lBBmeBQIICi0GRZRp5laGvoLK+QxDFJGBJIQRxJ8nw4ZpSXpYBJJT0NSxhQpCPCWo0wCDDO+47Oeg4cWXYHK8omxmEQIqWk0I7CQGYshXNlSZggEL4W05YhFud8uKTQtgwTTfJ0fbjFi20cx7herzQhfSc05anQUSrAGFeG85zbkwAAXIBJREFUcgps2VskzwsfZyTzcVZjvbVqASExRUYUSBr1mmdXkAEuEjSXY1orVZmWRprCj+Ggiy4EWvtCgGpT0mXap9EaJyxBGGBLgjmtNUophsNhmYshSNMRAkhqNbI0BQFhGBDECWFSJ1ASG0pffut0aWrjATQn0IUlHQwZ9XtI6TN4wkAglf+pFJAWPHr8gMeP7rO/9xRjcrAG6wxJnLCxscH1K9c5e+48m+cuUFtaI4zr1KKAuJx7JXwWkh+w5Z9cOKeFcpwAPyWkJ6Xtzb82n6MrqmB5mQGTZxlFIcmzgigO6HZ73Lz5IVdvXEWFgn5vhDO+LCrTaRkn9f+pyoIREhmokvWbcpf22SRCOqwxBGFZheJs2bzVd0lbX13n1Vdf4+d//uf54mtfYHttiyRJPBIa+LIuGXjmBlFmHRVFDgKiJKTRrKFEQBx6uN5TpzsQDiEjRvnRuIWhdt6kcgIMlqEz6NwQhR6mx0mkCrBCkjnj4XhjiVVIEkRIJ9CmoJ/nZEVOiCAKfM2mE+Ck9zNd2cy2Yhn0XbpKNjx8PxHjvE99sL9H93Cfdr2BkY5AKmKnkE6QG8POzsEYBLPWJ3p0u13SLIVyA8myFGcNOi8IlGKp02ZtdRUlHLrwtKregpGlUPhtQhtIU0uWawaDIaOjQ2yRk46GdPtHFDolS4dkWUqWampJjaSWUBjNME39RqcioqhZpmk6Go2EOJSk6YgkSVBh3Zc1hhG1JKHdbuGaDQ/k6HyMojscaZqRpSlhENCI2yAEvf4RR90jsiwlimM6DYVernG0K3jy8B6D/oAgVGxtbfLmmz/Da699gfX1Ner1OioIESWzIa60YoTD2XIm3H9Ebu1JAnsaYvtcOY9jIfWoqEL6hrdBgC4KiiJHKsmjR4+5e/e+j3MagwrUrF8rK4InMU4Dq1L4ZFDaUcLhpDdvwNFutrl25RrXr1/n+vXrfOlLX+LqlavUajUaUQ2/oXmCYmt9TqXO9TioD5Y8970WgyAEJKFSPq5obRnKOL5hSVV2fFYS6XyHbmMsTgq0Neg8Q0V1jPVhDmM9FYuSAgLf5S3Pc7KiwOI1ZhApAqn8wix7k0jpzbRqnFXgW2ZgZZla54UyLCtXHJCmqQ/sa01W5MQixAURaZ6zv/PE9/dsNLDWMhqN6PV6IAQy9KGpUAVoYxgNRmVoxBGFvhgqiqIx/5TWOaPRkHQ4QlvLKM3o9QceECoKXJohHSS1kJVoiTQLyWoBzjYRVpXgX4QKAtI8862WtCXP/FJaajdpNetk6QBrfUMtYQu6hz26vS71ep3NzS3s6iph4hFzpSRK+bBIoQ1IRVxT6KxA5zkgSeKabyMpBU6GbG1to1TIcDTi6ZOnXLt+nS+9/TaXLt+gXm+MTe4qr1nMWJw+MUQpRRREJ8rRM+Oc00K1SFuedDyPgFaaNAgU7UaTlWaLUPmWANevX+PM9nm+/s1vsb9/CExCNNObQxWGcNLXJ1YaOVCCJA5otVo0mk1ef+N1Pv/qq5w5d5bV5VU217ZoNVvEsU9iCELlY3FOUxQ5T58+pd/rezNUSlQYEgQJ1kEc+zAAwvMTRVHI5tqKb2HgfPOfqq2DKWlaBJPqCA9w+FxgFZQJ6EHAaDTCuJxcQ24cWZGjLCw1G9STOrnW9LOUXFskZdZTVbeIQ1Zt85wly/NxUkMYxYzhFTdJYtDWIEPFaDj051tDrgsstQkIUuTs7e8SBgFRHOCsb4tQryeM0ozD/UOM1rQaDdqtBuura2PNU41Bo9FASunN1zynyFPybMAoTTk4OsIYR5wk1JMAU4ZxVagYjXKMlcRJA2cdgQiJk4RCF2hjUGHk82fzlGFqaNTrIAWjNMMaQa2+RC2JiKOA1dVlssw/YxzFflNTASoMx/ODgLhqB4GDpOof6tdGFeKyeOZBbTRvfvFtdKGp1+s0Gg2CIGLCg3tyws50Ev9Jx0+cITQvHKede1LFARxHfEOpqAch1uaoAC5dOE9RGG7e/IzhMD32/VVoRjtThmgEtgQU2q02F8+d47/7v/6feeONLxDXaiSNJk56DeJzRj3gAp6YSgmB8r3qyDAEgSAIhI99WRgO+xgLKox4MhwicKysdjwxFxFG1wlkSF4U7O/vcffOXfb3D2i2Grz2hdcxxhKGIVmhx2ipCgLv15QAkywpPqy1DIYpUgW0Gw0atQa60AzzjJHxmjyqEtRFSf6M35SsMRTGN5nym47fUHKtffikFFPnPMu7KQRpmo41cpZn5EWBiSO01b6/aRTirGZ/f7cEpHwR+Gg45GBvjySO0ZFgNDBI6Rj0+9TqDVpLLYwxjEajsd9traVIR/SOumR5QahCBL5FXxhGFHjWjLTIMQiSRpMkqSHwHEdJLSnjp4I4SQjCcJwkEAQBQdlDVo6LrfE0nePKpKreUnif26kSBpluuwBCWJB+w4Np5ndZsm74NdjurI7fF4Jx7ej0ep2XH5iEG39qxveTLrwoA+hZn5l/bTpzyBjDoDfwmSRCEcchSjq++93v8KMfvTuVseNKH3JSBha4kvHAObY2N/ny21/icy++zPWrl7l8/iyjwZCiMAxGOUmjSZQkZKOMUW9AvZ7gnKbRrNFuNbx5qQ3GamQACE0UCYy25GlKlNSo1WKEsGid0+8d0u0e0mw2WG7XsTpE6wKTF4yGAwb9Lu12gzzPqdcbXltpXzBuq2QBKQmkoshy3n//A6J6i4uXr5PEEXFSpx4n4BxaW7JCo53vGl2Z29aVdCJCYpwuSbsNcRT5hSskRVFSxGDLhWjLFnk+VzepJVCa1pkuyHWBthrjfFJGUoux2vuNQRCQZRlCQLTUptWqk8QxYaAIAkWgJGtrywRRTFJvjsunptdCEkU0603KmBfOlZlRUlLVQvq2875KptT1BMo31kL4VEsvmFO1rKUAhTKYhI0EFL7GrkzEt2UmmI+1C+ugJJ2egJkCIe04lFORTYsyXox1SDFhivdruaLVdJWumJIROBa7Hr//n0A4F2nBytSdp8yv3pv+Of3e/GGtY6fX48PP7nBxe416vcaP332X77/3EU93HwDGV2A45wmJpSSJQ5qNJpfOneWN117lzS98kcuXrrKxsUWj3qDX6/KDH36bMIrY3NomCQRFNmAwOPITlRfYsITrC4fVAmGlzwk9GvDk0X3SvAcIdncO2X3aZXPjLFtbkihSxEFEmg7JRqlnCUgLgprClh2Um/U6+8Lx4N49zly4TGtp1eeuGp/Q4LQmFJJmGCFRPHiyw6uvv4EMIgprCVRCHISAYZQXjPKMNM98+poCK9y4EDvAl5AFQUAkQ4TyC9shGGUFeV74qpXAa9pQewvDKEdBTlQLqdcShJAU1odSNL6aZHN9ne21TrnwJmTa1WY5tpNhwoJR2YjOjcNY42iS8xxQXnOJ8rQpd4kpq6z015gKx1V/yxKNxuDBrupzzpGbbGZ9jT/rKnqRSrOBRxS9Rp08Ez5ho+LtcJW94VsH4rxWrdBuSVWkMImvMn42f4Hq/eq7HVWq6EKRAH6C3Fo/rouvdCw17xn+6KLPZYXl/Vt3ebq7x/ryEnle0BsV/hatQxrL0tIS586d49Lly7zyyiu8/uqrXL18mWaj4btXO3y2CIJe17LSWmKp00EXmr2HTxgMRyS1Oqtra7RbS57Lx8Fg0EfKgFarDc4x7B9xdLBHvRH5DBQp6LQbFHrE/Yd36Cy3qdUSBoMu2ha4wtAf9kD46pjhaEiajzzLQmm++o5ssqxJHREEIc1mi0gpoijm8uVrqChGW8NgNMIIH67Icl9jqI1BKEFYggxKBchAeGKwkmVPlOEpBBTah3kGwxHaea2jROBL66yP5xFKDnt9Hj96TKNWZyxXZUDSGuuRWzXrzkwXO1eULOM5ndYQpRbyJnspBKUwC6HK9eupUwSTovD5RJYZ5J/KZ55s/pVQzaeJVjH56fV2WkRhdt0KRCkek8+Um5HvDowVZuxezZ4nKmU/8S3nyaPdsV+OHc8dSpkGcebrNmdvbAEiO3UsZE8QvozJOEE/Nei9HrU4IUlaJELy1muv8uW33+Ly5cucO3+erc1NGo06YRh5s8eVSG2JdDhnMTYlUBn9o4fs7O2xu3dAfzDgwsVLOPocHvngb1HkdLtdrly5gmGTdDDi4OgAFUY4Anq9Hlob6s0aSa1JEMc0GnWvscIOy6sr1JI6cdIkCBOEDFiOI5rtNhcuXkYqxer6Vml6ObKsIMs0Z89dIIkTbFGQFxYhA/ISxAlEyQ3k8wAJwsD7j0GAVN4HCgOPWI4VUrmv58YwTEdkqQdfwJEkMfUkJitSbFlUbQOJiAJ+9P33vb+mAgKpyn4pHvYXQFBSl0xP46zvP0sQ7Qu+q/OgWqXWTgmwmFCnjJkCyzMr7TstfPNrbfo+pgsT5o/jXcpmj+MCPnXtarM4pmicN22xM9c4CQB6FjB0miJ7LrS2uvlFD3OSkAoxq+orhNBVDzg9lqXJEQaKeqPBlUtXePWVz/GVL36Ri+cvsHVuk3qzTi1JPAVkaeP7TIbpOJGnQjFGo5Sg1mj6AQgTlle3fIFze4mkVh9z6jbqdeIkodlsEoYhtaRJZ20DXJXU7BeXkJ45XJRggxCTChJRtiHwNowtBciNwR2LJC8KijxjZXmV5ZUVn2VjHCKoIYXB6txn0VjP6RsqReF8HEwpHy4JpJxQXeIBHSv9OOfax/36o5RhmuOs8eVYzTqmyCmKFIwmCMAIQaYNn338MY93dml3ln2BgApQ+LBIKBXKiZIa9WRgw9OMMdZokxYG5TqQcmyCjn04Sra86tSSkd23xT7dDZp2oRYVWMwfz8JETrYGBbhFqGuZeC8mRHLVOSddb9F71d/TVVXzx0+UW3sS9Dtz8wKfiTM+zU3s7DLUUBk4Es93u725yUsvvcz1Gzd4+aWX+fznXmV1eZlmvV5WRvh4nDFlIXfZScrfn+cMGmf74M9ZW99iY+OMF5zxxPk+A1KosWlUtZQYm1i+AMhv+1PWmXO+VYHXDGK8hjwGISbLTihfbQJjkENnGVEUEgYBeZ7R7x6NGz0pFYwrGwSWQApKGiSEBml9mEXK0s+irGlNM6JGHWRIfzji8KhLOkqRUUyaZawst0hqIXk+wmlbpr8ZonrC4WDE93/4Y3TFIoHzifN4M1mWPpgVvrTOMxVM5nMy+AKYLqyfGLiiRErHKtFVQIkoe5FNTMBATLiXxGQHn1n082uxGt/q9fn3nscFOy2aUJnYzIA701pSnSiIJ722SEB/as258IbFJMH92OBNqUNXake/o/oEbBUqAiGJo5i15Q5f+uJb/NzPfJUb165z5sw5ms02SZLMxDOt9SVRFRpmtPGsAtaXOHnwYzLIVRuC6UFZdL+VCYaY5JhW5y4adKlUCUiM08jHi9HZyaSPaRNFWRmCIKknDPp9cI7RaDS+P200RqalDzlpXRDHEblx6MyghadRwfhqmeFwCIVmfXkFEQc83NnhsNvDOdC54eDpE1577fMINMZkWG0x2mE1CPxu/2ff+AaNZodhf0AU+IoepM9MUsp3GUcwNquFFTNCN3O4si3Q+PVq/OzMvMhg0t59kutbDrtzJchDhdIcW3eLtNT0vcz7jydxWS267vw1Zs8Z3+XMa88yqU8T+kVg6aLjJ0Jr529kvCBLH1QKiXVl/d0YyBMstVucP3eO8+fO8YU33uDtt9/m0oWLLLVaZS8PH6eSKpgR/vnvnR74KKoyKyaNbOcn7DTnf/y+m31//hgLenVNpq85ucb8hjA9CYf7+9RqNQaDwbhDuHOuDNIXk9zb8rmEEHTTlN3+EFPmsprcJxQsd5Zpr6/SzzN27t7nzv0HqFCxs7vL22+9xbVr5+n3jyiMwQovsE47IhVwsPeY3/mffo/Vy1dZObPNcDhEFBJhIE5CGkmNOAx9mzwHUQk2lVzxx9YC4LOhpBhrwiqs4KbOnYBHk36nY/cHxgXsi8zC6blatOnOn/ssM3ZRxGHR3C8Cj55XK560lha9/59Mcy76svmb8y3nDY1ajddffZXXX3uNGzdu8NILL3L23Fnq9bqnDRzbPGWmEGJGAKvX54GoygxVZcxLlPV1Jw3Eonud3oGnz50BqeZ+Lhr8kyasWkgyCNjf2yMMI/I8JwiC8SZSUXjChM3BOcdgMCDPc/JCo7T1AhLViJcaKBliHbz/wUd8eOsjbGF48YUb1Os1PvfSS+Q64+joAOccWVGQ55pAQk1J7t/6iH/ze79LHio6zTpRFJAXOYEMqCU14ij2RctKVeUCfi5dZQEdX1gePXVjzedPKDVLafrPL/KT0NPTFvdJoMsi4TnN75s/TgOLFmnmk+b6pGPRZnGSxl90PNvn9FcYOxSy9OEElCQ13pdYWlqi0Wjy0os3+OqXv8wX3niDM9vbLLXbJGUdoMEhAu8/FiULGUJgpacIVHMI2byzPJ18sEiDLzqmd+vp8+dN52n/5bQd0h37e1b7VtcWUjIcDKjVPSCDmJR+VZtOFEWEpQk3jvnhExUcUDjIrU+guL/7lFu37/NkZ4ciy3njlc/x4tWr2EJjTE7W61E4gzWWNCsonCUOA+rCce/9H/Mf/s2/Qu8dcOlzn2MlqVGPYoTAd2QrQSCJGDeYHbe5c8cFsnpW/3q1AE830U6juJnfKJ9l/Sw6nmUiLgIx5+/jWddbJGjV64s2GajmtdrMy1fE8WssOk5vx4Cd2HPSf2kQKqwxREKwurbG1atXuHb1Kr/wCz/PSy+/zPLSMnGQECg1zsSvHiCQnq3cWlfuzj7rwjkHwiAwYyBGyKnE+JmH8xpn/D6Mg8HzAzdtEi3S8ovAgUUs9uPf/ZeVaONs0LmyGoSQngc2y5CBoijKbBvjc2xt5eOWfktAiBuDMpbCWgZZRq4tj/f32dnf5+79BwgpWW53+LkvvcWF7bOM+j0fZzW+7MngfBvGosAFPpG7Lgz3v/09vvnv/i2BHrGURDSjGo14mWa9g5CSIAqpNdosN9tEFWKrfIK8tpbCGqS1zC+h2UU4PT/VuE0p0wXjPi2Qp2mSeTP4eY7TzN75857lGy661vy9TzaeMotoDj6rfhvHOktydSFOBqTgGcIppRtTQijnK+M3l5d56623+OJbX+TatWucP3+e7e3tsujVs49jPJEudm4XtJWfUiYUe37HMcgy2aqrAZ4ym6qHF2IM2y86pgXsNLNi3oQ6LQG5WiCVpheiLEWbHsggGJ+XpqnXfs75RO8i81FIN+una6HJnMI4S1rkpEXOYbdLb9Cj2+3zdGefze1tvvTmm0gp6TRauEKz9/QxDucbITlHpnNf8yllyQxnaUYBB3fv8M0/+SNindGuhWROkA+HxKGvmNBG+4qROCEMQ+pxjFDKc2u7khVdCt+Jemr8WPD79NjCYp9t+tlP2gBPmtOT5mXRNSbzddzEfV7TtFpHlel7WhbczNpxE+E88RmdGyfR/9TCef3aNeIoYrXT4aXr13n7i2/x0vUbbGxsEdTrOOELgK2zE/nh9N1o/uHGgoQnY5rNcVzsH85rwUXfUf1+0i76rHucv8/yhbLEyquFCvoQCHKjEcafr432qXrWC11R5FSZNJW/rMuC5cEoYzAaMsoyur1u2ezXcfXqFb705lv0+wMKrUnTlP7hgU8cN55L1To8mZn1VkkYhDipsKMho8f3+Pq//Od0IkurUaMWRwy1Y380QDlDoCStZgOnDZFSKBxKlkzygDCaOIxR2IVjddJr80LyrM+dpr0WXfM0f/QkV+e0NTJ9/rywz6+9Rd952vcucpGqxPsJIfY8Nj05ThXO//c//ie+sj9OaNRqJFHsKx+sw5XAhvc/K5IkxoDAvA837eMtniAP7CwSytOEbP460z8Xvbdo8cz7DNPnjUEqwEqv7VUQAg6tDUJIX3iNz1JK0xGHh4feVLWmrJNMEQIPtpTNcq21ZEVBb+B5eDc2NlhdXmF5ZZl0lJKlGU+ePsYUXviMtWjfwcN3s7beL3TWooQkVAHCOqzN2L/7GR/+yR/TyUa0l/y81YKYmtYc9TKkMzhnuHbtGrc/uYsiIJQSZzXOCN9YCUs9jktM4fk2xvnX5jXHqRvfCcf8Yj/JGjrpM88DvpxkOj+Pdl9kIUwrqJM+N3GJfkrNefn8Zax145pDhEA7T/mBNThnqcieK1vTk0b5/hdVAsDk+6uCaK9xKh9tbKSK4zvVT+JrTD/0IpOmAmLmUwiP75KeocEJ4dkJcDjhPNoahggpx3WaeVEgZUAcRWitSeKEpaUOvV6PbrdHmqZI5QuOk7iGVHKcK7u9dYYzUpFlGUop+v0+jx8+whnP5aOtT1DHiTG3T4GnA8HnXhNKhZACYzWRUBw8fsjf/skfsZGPOLuyRNSoeUJlJ4mKgmh4iLIFoYTt7TM8ebDrNaeSJSWlZ/dzRhDHsU/kH+fzqvE4LjoWcUudNI/TSPxJ/tz8sWgDna4kGZ9n3dTGe7xD3knHIqWxSHv6+5jkAjtRbT5yChwEZqzIsiKmek04OMEqqY5ThTNSIU6WCxdfLxkIX2mPqnw6Pc6kcG6a5MshxGwCs7XGVylUgiMsjskkVeGUkxj8ngdxe5a5W2nxRTEz/3mFc2WrNilAVQwDZXcvo8lGKUVeeCCmLPBO3dCDINKzwC01l2m3VqBKnjCTDSGOfWjlqHdEf3hElntm+iqlEDxo5isjfBKCxZGXaC8l0bJP8fcVJEo4Ht+5w9f/+b9kOxFsrC+xstqhVeuAEKRFn6xQrLsOiU5pODCZ4cL5Cwz6XVr1daxQhMrn0jabTWpxkyj0fVUqLONZKPn0/E1bJovmb9pnW2QOzs/1IitqLJiu2uihqlLxn/G8Uicdz7P5zzfrKtvxMp2FKvCbgk+G9y/Mpz1Wf1bukGNSgL/oOFU4VZkVU8XknKsyXDzRUVBSVAghfNX/FEvcPGtc9fmZhrxCHHO0582m+ST7k0ys6ffnd9iZ8Slfm88YqnZyJ0TZ89MnnCulPLtfUVBkGUVJLDW+R8R4oI2zWO05WWWgxhSYDpChT2xPRymPnj6hKHLSLCXT6cz4VvdujQNbdWx2lNtYeb+gSsYAUU7i6GCfv/6jP6SuC86ubrG9tUSr1USqxIeKgoIoMNS1Y9TrsaEUObC63KErRCl81ldDYQhVQhD4+sqxWTQ3htU9z8/fomORRpxntlg0dyfN8ex3Lp7jqVcWWo/T2nvRMX8v43ualsq5+6oUFdM/F1589jsWHc+Mc1acocPh0JM6pSmdzhK1WlQyqnuahziOj/mI81+8iJlvkZBVr8/3YTw+KSc7/fPHSf5EJaTjahvh2Q5kGQYy2u+EoZSEtRqiXp/cz9TGYssepMZ5epEsz8m1p+vMSyrH4WjIcDgc0zb66hldip1PB6yAAp/9Vpr85XiGZWaRZxHUGA2BhMHBPv/z//3/wYU44OLmFue316m1Y6JaA0SME77pUG6HtOo19vb3SITf+Y0U1BtJyVzowBYIZ6nXIoTylCtKnrzJPWvcf9LjWddd7F+eDirh3IwsTX/HouudhtAKIcCJedn0n61SNeY2mtPu/6c2a6fNi3q9Tr1en3DqBN6uXqT95rXR/HvT13/W988/xEmARDUY8/5Q9fq8fzM9eJUmlFIilPQa0PisJel8uzrhPErumJ2oiuQ4K9nwtDEU2gtkXhSkWcYoSxnlKT7GW1UzWC/QTDp+j+/dgXCCoDST/UBP3q/aAyop2H3ylD/6vd9lJVFcXe1w9cImS2ttklYNJxOMDhGuIBB1MufQwuCGnkgrqMegFK1GnWGWenPQOZQQrC53AIsV/u/KRPtJNsTnOU6zcp71HZP3ShBmgapyrqxxLW9bTnVFrxI/5o+TKkUm8z69riaYzvgOFpjks/c0CzaedJwunMKbZzLwVBH+psoyKTkp+pmp5nalIefGd1y9zPR+Uz3ExDaXUwugDFEISg5aM9swdQwyiTHD+jFzCEoKC8b/qgGx1no+VDdpDwiepkNZ6bthS0mR5wxH3uy02lNqBtKDJhbfE1MbgzYWbQoKXXguW+25eLI896x62tOHWONjh77Ne1CGZBRV12RfpuQ1gUT4sEipV6USOKM964FVjPKCw16f3/5n/wy394TPba+xubVMZ71DXG+gohgVxjgnyQY5UoQEqk6r6QjTffKij1RLfg4KQ6ISTO7pPsNIEScRWZ6WFsSCtXECurnIp1z03vTrz3M4531wUcYe5fT1yv/cQn0mp5aN820Ups1TMb5CeUOT+5Nzkjt5pgoAFUzKWcXY+j9J6Coq2DEwBDOsfPPHqcLpiacWDebcMIhZzcbcBAkhJs15qgf1z+aBj8o0mdud7cStp8qfnXxzuVnI2cGv7lHAuLEPU+9PmusGRHGILNsXpGnq28qNhuPziqIYWwpSSpIwRgrhayezjFwbCmsotCFNR2ijS2E1JQ+P/73IU4rCa+cwDAkiz7AghfI0mLaMnboJwu18226c8lysDksYhQhjSEcZR/0Bf/4X/4GbH77Pm2e3WG+3aC61CGp1onqLsOyelY5GqDDAuBhlJMXgiDgQHB7ssrZ+AYNACV+8nRUGrKaz1Mbo4nj1PsfDDqdpvNOspEVI7fznjr/neW+lmMUm5mXyuK84LjIda7vyr9KnXnD/c5edfZYpC0dMEhWcc7jpzLV5a89WPENybHUt0vbVcXqx9Vz63SxaNqXPxzc9a3Iu9g8W/y2mHmbe1/QsB8cnc/qB5/1WV16nGjhT+oRKKaI4wdNkeMBFCUUQJTihyNIhRZ4hpaTZbI6T1a216EKXCQM5hTHkxo6F0xNi2ZLseMRgNPTt4srmPFEUj/1zbTRplpVM7bMm1BiokiACUe7eAiFVqaFhkGt2Dg4ZZBlxrYaQAVFco91sU6vVCaMYGQQgBWGcgLE4FVKQIka+w5kyjlgFaOuT8J3RKOmwhabTbiGsD3V5Hp3jjanmEwxOE75nIeuLPrvoc3KuhtLrAb9Jn6aN58GmCcgojq3h6vNlIdzCZzrp+mOsZe51mKzvseIZuzc/peac3z0mN1Y+LIuD0vPa6vjnZyfNP1SZbTv9kJUJIRYPvLPOxwHnvnc8AGIyQFWZll/4vusw5eQ6KQlVjJCeGLpeiwkCPzQVtWOWZRSFptDaN0a1Bm0dmdZkeUFRZIzSlP5gQJb7vFoVBNSTBlKG3t9E0O8Px2at58l1M3C6K03tiiA6kL7leq4NBsnTwx55XpC02iytrFFrND2Teb1BEtWQMkAFISoMcUr6DmNaY5xnm8c6YqlwWUFQ9VOR1vc2cYblTptamWzibAlIOVci2HPWySkm6TQyf2zeTjF3q3lfLLiLv/N5BXM2EcZ3BDgJKPJLb7HgzK/v6TGxCzaasX9qF8vCScdPRI05LTBu7AP6nX38IOOdbMqsnVxk+ooT53kmo6JKBZyCv92sgE7vrD5jyftyrsoFHXOWToFVU7ss+JiYZ7KTpXbzSeBBICiyEYPBAGcdaZaOr59rjXO+oibNcjKjGY5GDNOMYTpEFzmBCljqLPnYpHMYB4N+lZCQ0Wo2abWa47pN3xekCgc4oGzMI1zZck5Q5AWFddx59JD9fo/NjU2aScLmxhnCOME4RxLGvvtyHBOEEUIpnBKowCGlQueeHjOOE/Jhik4zMBqpIk91YjRYTb3W8cXjYuKrVwBINfbTc3CaWVuF4qbXz/z8TQsNMO7XMv7OacCA45qm0kD+2pU6mVUri34rl+BCYfZ+4cQffBaIOR2FmKzpKRko35BzY/is43ThFGJm95jdoeTYsvVMAILJHZWm2JSzPR48N/9a9RV26nxRFvAy42DPmxUVdyuADBSiKr+q/NDpgZ8eD+fzSP1kF+T5iCDwvS6zPGc0HHnGv5KBzlQhEuPLtwajIaM0JSuZx8GzPMRJA2lBldpwZApu37tHXmja7TbLq0s0Gw0vkMLXSwZKju+zSjQPAq+xtLagJN0s5ebHH9EdDjl74Tztdo2loM52a80Tg5mMREIYJ8go8Y1eVeCb/Vpf2SdkgLOS3FrsaEShDsnzIWESkmMwzgunCgOf5ADjEM/08bzm7DySPn6+07SFoNoJZqerCthjfBfvaqOuPKty3UyJR3m96fMqV2xyjpg6ZfJdYnwvYi45Yv65Fr7mhIfzxlq4RHRxIDxn7qKxWXQ8k+BLTPltJ93UtIM8fvC5G5i9ieM70VgTlwNdAUvOzu6W06CO79g1ofeYu7nxLblqX5geQ2vJ0pwoVL7SY3fPd2Auzw+Q3ldzvpQr1QXdwZBBmlHoAid8koIse7dYY3AGhFQUxrB7sM/T/T2ieo2V5hLNZpM4jIiCEIHnro2CssUezjOmBF6bF0aDUoxswcH+Hh9/8gmD4ZCXXn6p1EaWRi1BCcEoz3B1QRQFCLwp6Vu1T/x3p03Zvl5RaE0UhvTTFKMLIipzzFKLY5rNZgXL+fQKV4nG8Xmt5mPWqpp97xiKPnX+8WPBa9V5Xg3NJADMaKvJtx67xLhfJ2Kyzqprjr9m6nvmTPHTMtZmXxNz/6bAp+MQzTOP5+KtPQnYOY6KnZydc9IEzpqzdmYSK79lPoMmCAKiCvGcQg/nd2jPIA4eCPUwukdyrWeyUzA4OERJRV2FHI5GVPwEDm8SGGtJjWaQpaSF9pU4cYw22jcUqrS69YKwc3jI090dsqKg1W6ztrJCqELiOC4Z9LyQBOPyutIUDwKM8EKa64L9vX1GznDU77FzeMCL11+g0/YcvOudVUIV8ODhE1INMq4jw5goDglDH6KpitvDIEAbV9bX+sa9ojDkw4w0TYlaHt7HWmpJnSSKcbpKQXPjSMO0Bl0kaPNzO2+uzgvxYp+y+n2x+XeaObhobS363Emby2nXf5ZZO3nNja236c/6e5vlqXqe4yfmrT3tJucTyk/6/OLd09NJCjHxIYui8DyuJWI6n+ww3fW5em36e6wUY9PFFQZpSx5Wk6PzIXo4QowyNAKrJKDHaXOUfnDhfH9MXQbkrQCjC/9amStcWINUAR9+/DG7+/s0mk221jdYajRphnVUGJDUap7WsvKjbBnHDT2qShhgnWHn6IDdg33iWg2c4rPP7rDaWeX85lkaYYwVlkZUB+04OByxvHEBqQqUqiNFTKDCcU1nZfVUfS6lEH5TKwxae19alXPgNXnou4XlPpfWu+eLQZP530/SoPMb+aLXJ1prdhOYXydyQabSovtY9P70fS1Ckqc/d1K4aJEmnRduIcTY+ps+xxjjy+/kcR6lk45nd7a23u+YhpXnJ2h2EGcBgKpY2sFCtGoCKfseJdOvhWFIVFZ7VHWQ88DCovvxh+8yrakTOUuApwpxo5z8aJdC9wmEpCG9H3Z41Gdv2EOGEXHoFznON4UtjDejjfXNboo8xzmP5A6zAftHR3zvu++wsrzCxtYW586eZbnZJJEBtTDGBarksq2Gp9wASnY7AsV+r8udhw84GgxY6iwzSDMe3LtHq9nihavXaDcaBM43cG3V6ty795h61OBrP/srHH7yLkHY9J3LjCFG4Mqu1qaktbTGIIX0TXrDAKd9p9vcGYLS3OsstUhHQ6IqQ2bafVsACM2vgXkBna9SOW3eFq29+e+ZAC3Pp1Gnv+ekTWLRa4sY52cFGS8PQiDKhsyibME45lSmWt9+/QeBgpKl0ZYUPQ6fj32SEJ6uOUvkk/kHqtzDhWaK9TdcOvel5e0d4ZKdvXrIqgrDtxLPfaA99C35pid0kVAu2p2nB1M6jTQjMldjePgYM7xH0FhB6pgwK7lcQ4cIwElLEktaJmGQDxjpHoEMCIMGECCtQ1pw2mKMJh9lGOf47O597j19THc45OrFK1w8e47V1VVqcUQc+e7WSqlxMx6DJS18vxMRCqIk5uDwiHt3HrPb65EVhiBMODwYsLe3g7YZr7zwIsvLS6hQoayl2WhghCPPMl66cJ4rL17nn37rr+ltDpHnW1inx+0JVRDgygZHYRDgRICSAakQiNwgC00qDKIoaIaKdiPBOYuVU0LkKtpoZsZ7Uf7p9FFZNouwhxMBEVEmoozdv0W1krNCN50iusiXrbTWvPZc5IJNJxOcds9S+EZJ41Cc876llAIx7mJWnlsKizMGCEpFVQKZwjHu/rzgeK4450lO/MlabOIvzpgv5e2OQx9TnwnDEEomhJMm/qTJqo6ZXF4hcakh7H/M6P676N5DwuUzhO3zFEZgtS9S1s4zoIdhRBJLVBRgTYYpHLYCnoRnFMyyjH6u2d074L333+fBw0e88Mor3HjhZS5sbFKPE89gJwVhoEqlKFHI0oeFJKp7czgIePDoKTc/u0V3OELGEbm1ZP0Bw8GIWhhy48o11ldWSaIEiWedqNdqpGlBLWgQbAastJfZXFlnv9cjH62hQosJ/cqxwoKQKBWC9An9zihUEI4FzxqNsoZareUZGqYqbiYDf7JbskgzTq+Nk0Ch0xadc8cFcN4UPM1qWqSRT/ONFwngafd5kts2Bs8W3GMV3VhgdJ84FKebtXM3Or5ZL7EnPIz13aanfb+xmeB8izxjZmgu/QOUVREwFyw+ecDnj5md2kG322f06Tdomz1cr0f/6WOWzvZJozWCsI5xEo2jkIq+Echaw3fpchEudhht0QiOBkOCQDAa9fnok0/52+9+h+0z5/iFX/p5Ll+4TKNWJ1YhSoiyZYIvWFbCW63WWIwAVECa5/SzlDsPHnLz448ZFYXvui0EptwI2vUGF8+d48zaBs24hgKkcywtLxHGMUdHQwYaaDY46A955Y3Xef97f8mVow3OJXWcFSWJtMNaRyQUQlikihBBiLGOvMjJ0xGRLshHfVrntsauxLw5aqcY9ie0pLPHSRv1ImFYZJaOP+s/ODPf098nOF4AfRIYNH9P06GceUFfpF2ra8z/7t8/SYvPkmnPbE5V3pEQZfeDiSAvOk43a6vdYMqfoLqtBQPiKl0/97lqNxbCtzuvsm9gWhAZC+cioGHRYJ000QAOSdyqc3fnFtFqCxUvced73+JCamlce5MgTPy51mIt9IcpCoWMQoJAlil7Hvg56B9y89YtHj3dYXf/gF/71V9m88wZlpptGlEN5ZQP5ltX+nW+KZwon8cGDhWGPD045L2Pb/FgZ4e9ox6Ftp58W4AsO2t3Wm3Ob51hc2WVJAiRpVvabrWIophCF+wf9vjDv/xrskjyj37l11i9coHi0xZ39g9Y6SyRGG8eGzdBs0GgLYggRAbKd2EOJYF0GAFRHM3O8XiTc8dSDE/SgPO+2jzwMj+X0+eOFzfHN2JROWjlzM4fJ7k4J50z/xww2dgXZTUtsiRO+m7/jCdo77GWLE13MemWveh4hlkrxom6dirMMW/mjM8XPtPFJ7Pb8YMGQVWUXTWGqYRYlBrz+GCcBhpUkzxtAs/7EgbQSZNw6TxZ7xF1DO2Ll/l3332PXzh7g1ozJml2sEIS1SJqR4LesCC2ip2nTzjq9jnq9YnrTZpLy3z+82/wWhD4kEgQQJnnKZxAON+81gnPWKAUCKXQ1lBYy+5Rj5uf3uLp3h67h4dYKdAuR4QKU/brTJKYpVaL5XaHJPa+H04TqYB203d3Nhb2uyNuPXjC6tYGhA5hC+rtDuevXefDb/4ZL2yt0TEdcJOW9hKPhdcB43xSQrNxiDS5L1tTCYIA6cBp44lGT2gceZpJepJZeBLquejc6bZJVYx7mhEeMbtRz9/PsyysedBq/jOT3wUVk57f20QJaHrrcAJ0urGfLARj4G+R4hBSj012Yb0cnELS8OxQykx45BRXYeJHmtJ3LP3I6ZsTk5Q87/JUpTPTJtTxcMyiHXfRwE4PSCAsWMmFV3+JH3/991kTO7j2kNd+7S1an79Cp7OJNSEOiZWGuLPOrU92SJImalmS1NpculQjjBKixCfKGwdaG/K88OCW9F3HEIKsyCisJteap70uB70uT3af8tm9e/SGPkFdG00uHHlR4MrayVDFtOoNVjtLrC8vc2F7m9XOMnEQEIWSOPTJBcYI9g97PH26y9rmJnk9pt/do55EtKKE/ae7DI6O0Fle5uN6ehNXLi4lBEEUEiGwpqBWrxEFitwaZBD7vjPTi316YT1jwZ8mbIt8uWddpxJPx1Reqps1FU/77pPu7SdZR0wyYgCPdlfr16/bKakSpbktvBtx0vf7v8ffUgKrP6XmHN/YAkIsIXw1Q1EUAJMibKXKGreThWfRDjqtDeffm89QmgGZFvillbkahpJs+SLbb/4Ke9/8Z1xZkjw4esiT27cIX1gmChKUkERRDSEU62ugNSS1GstBgHAOazVGa5wr/G5nBM1aCwOMspzDfpdev8/+qMd7H90ktZqnh/v00yFJs4EIFLmwHB0dIoUgDiO0NiRxzNrKGqv1VbZW17l84SxL9YRYSSLpidCslzCKPGM4yknzgs3tLQ4GI2798Fuc3V6js9Qg6/b59NbHLFmLLnKyLCOJQ5++V3IfSesQ0qPgMogAgS5ysAVJVB+XYc2P/aK/p+dg+vfTAMOT5m/Rd8zHEaufz4umzmutk7qRnarZ5xTRDB9SabbOgFQVevoczz+jqY+9Ozl+osR3mGjIKmunSuCuzvG1uR5OftYkn7brnTRxiyZm/hxv/oBRAuECNs5dQ597g4e3vs32ygr//g/+gN/v/SnLrQ4vv3gDJzVho0l79QLLZy6h2kuoIGBYVqMoGSCDgINel0dPd+gNU4ZZxv7RER/f/oydvT1Sq30tX6CIkpgwikgHKbnRgELh0/USQs5vbrK5tsb6yhpbK+t02m2i0GO6gfD8QE5QZh8ZZBRQi2JcA3YOu/zwR++wFEv+zlfeohgNuHv3M7r9I5oSCqvL9EaDogxBOYdSAULFKKEI44Kt7U0eHx3SuXCOWuQrZKoMrZMAlmr8FwnXacdJuMBJPt70MT+v82bp8wjAos+fdP2pP6jyPed9Z+8uLl53z7vGp/3wk45ThVMXeakh/WRXKrvqsqyUN7SnvITyb6j2hGmhnr+R01jWTzumSb9O3hUFBkndDrA658wXfolbQZ1e/zYrTcV3vvkdsmiNf/jzP0NzpU6aGw6Kgne//Q22XngBGUXcffiYmx9/SmdljV6a8qP336O91KHb6/HBRzfpjYa0V1ZwQpLUa0RRRIQiH6VI4wjDiHrYQAURrUaTM+vrnN/cZr2zTKveoBbFBFUCvvGpgUZKrPOhF1uiQcPRkN4wZa/b58GTHa5fvcyL1y9hRMFo/5Cj/hE2ABkEfpyNJ+ryyOCkHtNajQx8f9I4Sbh98wM+d+Uq9dW6Z45zxx2gZy38RcjsvCaaZ+Gb3cyPd0mfv/ZJx7M299PAp+nPz282/kdVyje1tkuQ77ms9LELt/hkIZhhZVh0nCqcxvr2dEJ4/1EFomQOmFbv3j6f+JOTZN/qOMn/mN+lT9pJ5nfKk46ZRYNCOEns/KLPklWuvP417nzj97l+7QZ2P+X9793lb7/+Z7zxpc8T9B2jkWKt3iZ7usfDwRE7vS47Ow9578Mf8/Bwn0Iqku4eVhe0OwnL603qzTZhXEOpiEAGFHlBXKvRbi0RRTGtZovttQ22NtZpNxq0agmRVATCMxAggjHoIYTPc83ynGGWMdA5VgpyXdDvD6hFEV957TVaYQNjHCOtebLTB1WnVl/GdXco+gV5PyVIasg4xlnfSsNDQgUi95lBIgg4v77Bg/c/4szZGwilUG52oc4XvZ80n/NA3iLzcdpleRaIc5K5eWyOnyGAi9bSSajx7HdUP6sQCGMf3E3Zr4uuY2bQ2vl7m+LxEALKXOqTjmek71lqteTEB/CI7HTr7Nkd6USTYeY7ju+88+f/JCbWBFSwBMILqRGK3ClE0OD6z/wqD/4m5cKLfVTUpBsEfNodcrmV0IhzXCQ5tFCLEoQ74vzWJutry3QOlpBhRJHlOKNpttvIIOZomJFb4VsXCMnm2gab6xvU4zrtRqsMgYQoKYmCgDj0Lfa8YAqM9Anwxk54ikyZuN4KEzSOtaVlou0IW2iPZhYabTOKvE+e94iTkNXVNUZPHjEaZugsw+kCp7XnHUYAEuEMEokqs5UunN3imx/cQooKdTw+d88yvWC+pvL0+V50nGZG/zSuz0nnLrrHRejt3CfLk8s1CmPE9aSxcTDuBzT/LPP3cVobwlOFM4yiGdSuAn3mhWJ+Bz1uJhw3ZxY55tXvJ+1u0w/ybPpCh3CW3Am08Ix6SoKLlmhfeYNmkLK3+5Ta9g1e/c/+AWb4iEF3SEd02Pv0DlsbG5y/chldFHR7fW49ekCa5+R5ysrSEkIFGBmwezTk6e4h7WaNq5cu02m2adVbxCokVp6jCOUmvTmpkhT8zpoZT2WS555G0zpLEASEMqAeJagoQjuDyTWu0ARBiEHgkpD97hFZaEhzjVhucGByBk7THfaIsyZBlhGpGlUsRTjpwwHGEgUK53KWO01wRZl7e7ppeZLGWzSvpyGjp722SOBO0t6L1tyiDf4ky+1ZFtv8M0yvy5M3E0qTdja/GE5v/7HoeGbJ2PTviwZj+iHmH3YR8jr92erm5691jGF7wfdWJtf8e+O/rU/4NjIAEVIXDuG835xs3eCg+4iVtff417/3z/kX//JP+MVfeZUvffVXWN5Y48LFlCyUWKWIRUS81CCMl3iwv+cD9hLiOEIFEaursL2Z0ggV2+trxFGCFAolg3GISCoIowDPJg/GQlYYjDYYWyAQ1MKIRpyU3coE1jiccWSZB5W0MUQq8NUNokZkI/p7H/PuOx+ghQAtqNXb3HnyiDBy6AjOhIIw9EkfEryJG0QUFBR5itYZm5ur1JKwrN08KbTA2FSb35gXzfHzAkXTa2XRNSrwsZrrRRvxad93mjk+fTyPqXzsXqfCPXOVbuNrzvvhJ+X4nnQ8l3BOC4y1vpq7igT5CZ1UeDsW70SLkLXqZuUUgx4AM4nuAl9aWV1zsRk8ea1aLJ6KxAmftq2cQbiSDjPuUD/7eezjD3jx2hO6f/M+l/pnqN39kNsf3+PuwVNWL22j6ssY2qxunUfHAdtxwtOnByRxgJQe8Gk1Gix3BIkQ1JOktCxAqgApfDG4ED5oba3B4dP0HF5g64E3h11ZOyqlZ8/PC80gzzDWURjtGxbVG+RWUwv6RK7gwnrIZ9Eenbogz464/HqDMIc8zuiLLj3XoB42CGsJcaONM9Dr9UlNTqgUsUwItcWZDPAmr7WeOsE4xvnAci5GcJJgnKYFn+eYzzCaTxV83us9j4Z+nvuYFqzZlhFT/qSb+KAgEdIdu0b1s9qs/XVLV+CU+3iuUMr8g8kyduZcmegr5KTjNM7H52BGGBeZGjPajwlP6Nh88H/hKkpBYEJz6MdkjA1XA1nFwqohs76XSzlU3uTQOe3mGvmFV2lf+Yzr3SfUNs8gzl+kYxokq+t88sl7WPGUR/sj3vi5rxG1m6w3AhpnmhgEhXYYJ4lkgFIhoajAMkEQKM+sV2aLGFNRVfiFHoaBLy+yFmsKz0srFUp5ZBwMuUkZ5kOePHlCkiSsLa+gixFWSqIgQhcGdEIjWSJMH7PdjtCdOqMso53ECKVRy4KtF88S15ZRjWV6R12ifow5VOx9eo+NpQ2ynX2+/a3v83d++ZeoJbVxywc7VggeVpzm0/lJ/MnqmF8LJ2nMRT5v9XclIPPhl+f1N+e/Y95CO+16MzJQounT1/Nrr1x1kuPXc67smev87/hTnfkpfU5XXmj6gcaDxuzAVb9bV1JuLDCBnmcQq58zXKDu2WjfIs1cHfPcNdIZUhvQvPQWL7zxgOzoEfeCGBk2Wd5apnPpItfWNrn1/ne5/9Hf0P/WPj//d/8RRWqRoUAREKsAZySj/pC4VkNFIaGKyvI2nyyujUbrAotv/6fGDXYtRa7HDXanw1RCCA8OOYuUFl2M2Dy3zcpyhyiMPOaa5fR7fUbdI65dvc4737zN5tpZ2nVJVx6idYGzhvdvvsfK5lkuXlsFV5A06yTNGsVSk7TbZf/pIzaWWnz8ox9w99pVLl97ERUmaLw15AvdZkugFs3XSX8v8tem/z5NCOZ/Ts/zs45F58wX5k9//0m+6KL79oeFqW7q47UnRZk5drIPO/37sziVnoumZN4RlkJMuJMWmDUVr89pSBTMDRQTFKz67ul7mDZjTwKU5n9Wn5k2SZxzOGFxSUim26xe+wpX9/f53/6X3+ef/t/+P7z9ykvcuH6VsLHEUKWcWV8iZ8RnH32bWussFy+9QC1qgvC8PdYNkcohwgTnvDZM09Q/H3hWOwEWSxhIXMmfK5xDYgniCCGiMv8YtDFe+yvJ4PETrly6wvbWNqEKJuPuFK2mQac9Pnky4t7TPpcvhWzFNQKT8eDRPqPhiHMXLtNutXjw6AGd1S3qpo62lsPuIXFNsjd6iNEFv/iVM7TqmsHuY1xYo7W6jhAKaTWe22VWPE/bHKfPOU1YT1qUizCE+e+Z/vw0bjGPQSy69mn+6aLPnSa41THT7Fmc3BxpOrl+XuEsOp47Q2j6ZhFT+fXzD1SqcMditrWTKhcAKpYAD0A8eydbNNjT78+3lqveN1hkkRIRk3au0/jcz/Hlt37IprTs3b2DXm5w+YWzLL30M6Sqw9HhIf3DB7zz/T/gcPcaG2uXeelzbxHHLbqDko+ISts7mo06hdb4fiiag6NDdnf3WFtbZTAYsr+/RxAEtDsdlpY7pFlGlqWosmKn3W4TxglRWCNQAbaACkxyOIzxheqRgI21NX7tN/8b2pHh8a3vsFx3bC2vcPvoDu2kRpxEPPj0IcJI4tUtMiw069x8f5evfPWX2X/6gF5vjwff/Rb3n/ZZvfgCP/fLfw+U75Dm62tKFHJqDGfn9WTk/cS5XnDMm7InXWfROc86FpnRi69VOVAnobG+FWb1+/ENY+Jzzr9vpkrvquM0BSZOe8hR79AxZWJOnpSFPR4mmvZ4bmQF7py8M1YaEeafYPLZxf6Dc5Tcr1Pa0VWQ1ewgCCF8h2rhe46kKsKEOQ+//yd89of/X2zvgJd/6ddw6y9CsooZHLLz4GO+/+53Wapp7v3oPV7cvMFhb8it3V02Xn6V5bPXef2Lb9LZ3CJzkkBG6OEAbVJULaDT6NBpL3uNWG5csuTMrZ6LiYcN+NK6LNeMstS3nB8M6PV7DAYDUBFHvSM+d/0SUb3F//LPf5/LG0ssj+4xOLhPWIvBWc6cPcvW+YsEcRMnIxrtTeKVdUTSRA81f/i//jYHd2/zn/3dn6e11CGzkvaZsxDXEYG3BBQSIXwxw/w4zu/L0xaWtRPNMP368WuImYV80pqaPn96XZ12/qL3pu+14i9eJIdOWF+cUH1mfM1qs6oqtkQpy+V5YvGG5JxDugkI6pwbE5ZvXLi0UNWfqjkrZHaRcM4r41n7fdYeP+mYFdLqNT8Ax889/rnpG5o2iRedNzYfhAChMEIQUBDbEZkWXHjpbVR3yHt/+e955+vf5vGTPyVOOgwy+NN/9+f81m/8Om998QYD+YBzFy6QfvYRTdvj9djw8V/+Ke89fMjlL/0ML33pqzRaKyjrGYxSkRE4B9ai5nzmMRgwNQ7VIjbCEkhBs5ZQjyOWllpIecYTXAiJcwZlLYNRwWvXX+Cli9v84T/5cxp6yPrKKlZajuQRl19aob6+jlEBtWQZEdYwTiIJ2FjZ5OdefZ0w8mPSajWhsEhlENIACpzD4IXT75uuGvESfRyP9njzhJMzhWZmzR0vgJ4/TjM3F2nURZ9fdHiQBsqM02PrxzmHk1OKZdq8LkeASjDhmFCOrzE1Bo7JZjUc+naQjUZj4f3Bc4ZSjtnFzs0gq7O73sQsXWR+zvuS06+ddpyk/udBp0UsCtN+qrMWKyROSiwG6QyBVmianPviLxMkm/zVb/8TamaPx49u85efdLlrc/7Hf/+n3H38AioPcEWHp58NGO0MCEIYJEM++/Cb3E93ubVzh//iH/wWSeBZ96SzSF/GvfB5F5lb/oErJNzv0IHwrRuwDik0AosxknZc45feeoOb3/kGNy5cZX2pjXAW5zRRErLz8QP237vFK299EasMUhZgDR989x2Wk4RAhmhrSeIY6SQUBVIqX9cZxlgpsdLjCEbrSe0iAm1sqVknYa8Kmaysmenxn573adfjeQWtutZJrz9Lqx5/3efKLjxbLN5UTjeJZ+VhWjiVUjhj0UXB0dERQkqWlpZIkoSTjudmQpi572OnzaNQk2Br5fz6SZAn7oSVUB+/hcUgz/heTgAPqvuYPw8EgTM4U6CFBiTSRQjjyBE0Pv8iX2j8X3jvX/8uWz/6If/7ayvcXu7yaXfIn/3guzzuj/i97/+AF9ttfunl63TvHHD3oyd0DofU9mBvZ0T381+ieeM6mcwoApBGnFi8PH2/VfH42FcuTXwhwE7leQoLjgAjIqROsUdPeHDzB5zd3qa2sooQEBQ5Nh2CdazGdcLCInWKNjkHO/t0733Ki2euEDlD7iDPCkxuAEEU5cgowkUFLo6QjTr7h4eEQUCSJBjtO5YrIT06SeVjTcba03OacVbZ/GKeFsqTSJsXvf48G/n8OScBm9M+oqsGdzIjVBbA/Hef5JpNK4fpaIMQZex6lHJ0dES9Xqfdbj/zeU71OYdH+67SNkKIceKBELNo7bRZUJk282jaZDKOO9L+53S2//EKh0UD/zwAQqV5KiffOYewBtAUSmCJkCZGOYuTA5TWuELwtHfIzXe+zg/+8H8l6fVReURvkHFnb4+7/QGBLtiMJNcunSVaWUYlq9z46tdoXb3BK1/4EgkSKR2F8jQooryX8SYz/b+YJbESoqy6d1NmvvRhKut89bwWCh3UCEeHPPjeXzB8+ClrZ88jkgaxlCitSYc9RrZAdVrYRoIKBasr6/zh7/wrziRLvHDxKipu4Op1VL3mM4ksKCuxCEgikrVVwtV1DvtdnLPUkxrD/gCEwDpBTUV+7gBrBEIJrNDeM3O+j6mSwufvOtCuimlP/Ew71jaAmwUST/o5M98nrQ8mmIS1vndNNZyi5EmGydquPu/KRAzrjmdAVQx7Ek9zY+UIoRzoyEdYROFJWU0AwiKERTjBoJcyHPVotVrU6rUZjZosLf/kPqcrG8yWEjV56Ln82sr5rxYYnIzUTYCEWQ2LEFRVAK5sAsSUCTW9rU3vguO2DAt251LFgJAT/E04nAKcRDqBwoHIAYFwCUJYCC3Lq1u8+bW/z5WXX+fr//pfcPu7f0PsHJejJtdtE1cUZNkIpQqW6oL185ucf+EqZ9/8MsLFSJ0DDi0lAoui5PCtLITSjXF28lzGmFnNUhI6W2vAMN4cwWKdwZgcOTog233KdquNcQVS+DaHttAYnZIOj3hw/wN06EjqNd7ZGfDxtz7gys/+HfTgCGFyhMlJBwPCVoMoTLBGYIWkCBVJPaa7s8OwGNLqtDjYeUSnvkTc7lA4wc0fvcve3lPeeO0N8ty3dSBI6dQDFCGZqGGFQrjCz6uKwHmG+Uqoq+l1lILM8ZTAZ2nMyji15XoVJSu2NQZnvdUWhCHWeNPc4cnmrNMEJdFcoXUZ0vNVQkopiqIgjCb8SkIItCswaYgiQCaWNHcIExK4IcgCrWNQMcalQEHaH6EzQWd5lSBUvruZq3zZxV204SeIc04GAd9kds6MnBbARf5hpRWklDP0i2NQQM6jf5VuOX4P0z7wvBkxHz6ZIIQz+yuOiqrQ757VCtFKYq1EOEVAyMb5V/j7/9017ty5yV//239J9vAet999l5Wkzvb2FivtBjiLHvQ5+OxTLr/0BUwEhTBoAUMrSJwdZ1RVR9U0WIpJg+LpPOOqmH3WBJsaGiEJHLgiR0lLkXWR9BFYUhGirCYQBY/vfcjOwUNUIjmkzdf/9Iesxh3uffwxgyd3aTbqBCqhHXcwrQZuqUlYi1HNGkFUQxQho0c7qDCgKLocPt6ltnWBOI7JBikvbJ7Fnt1ABBZSww++9R2y7i4X1gPq7RorL30F11wnJyQkJ3S5FyM5wTPGFlSJB8yL4TNNWef982p0K/egyAuyLGM48N9ZqyVEUQx4cvA0HZa5zbC8soK11ieGWOsZ+pWi1+tRF4IsyxBAnMToQDAwXewwI+4Z4rCBkUf0wwEyqyFdF2cfEqomaR7R6Kzg5MhzH1e5amWG2PGnnRzPZnyfs7kXmaTzgFHV+m3a56zOzfN8YVsFW7LgVRqwos+ssnir+ZkXvOnFO61JXbl7Voq/+nt8j0KirQYcSkmfUK4kqdNYZYlcQE0ECAOFdVw+c5mr/6f/nqPdPT5494d093f57OP3+fD+fYxOeev6Fm+//SaECs+jZAk0NJxCKIGnGhIzQleZr/MAySIepelnt/hkhVAECBFRKMHT/afUJIT7B6ggQVvL0WjI+zfvkAkHUchO/ynvPz4kZshHT3ZZaobUQkG70WKz1qDeblJfarHS6bC5tk6uHOq9H2JHKcvb2+ROcf/OI2ovjejvPaLb7bMd13EqpbA5ZIpzdLl970N++IPbLF8+z9uXXiFZWscJicts6XuLMQPPDBg0Z/lMNlZ7DDSaHxMzF2ut/vX7fYSK2dvb4/LyFYbDIUmSMMozgjAkkL6eVlvDcDgcm8FZr8soTZFCEtfrHBwdsra+Tq4LpLZE1iGTAHP0KaOdH5IkS6yufgWTf8re7k2MqhGtv0lDXUSMBE45cjcq17jAGE+jGYYRtfbyseeC5+hsbadMrUqQpgduUaB/WuNW71fa0jGFaEk5DtUYY3FWIEOJMZYsL1BKEQa+h4gQcyCCc545ztox/60QnmHdGK8JXdnzxFpLnutx86M8z9Ha0Gg1ydIhed6n2aphrCFGYguBtgV9lyGRhDIgEREGRWv1LF/9uxcwzjAc9hilI+I4JAotgphhBk45VORQUhAYgXHGV5k4NxbOsRnuFhNnCyF8CdnU81WHdmX6WGoJwwaX33iLv/qDj/jGv/ljGklA3AgI4ggjJYe9HmfOnaXZqrFdc1z6mYsEqk69FhGonFBJdKG4tzfk+3c/Yq+b4XLB+lKTlfUOV29cYf/2TZY31iGoIwrHw3ffobWxzHsf3SIM67zxyhWGezvcev8zev0RIpKsnd3k3OYFHu3s8PF3fsitTz7mv/1H/4i4sYwToLDj2PR0nNOWVgYwY2FVG/38OhuPGYw9n6q/DkCr3WaUWxrtFqMsRTvfjTyuJUgpSLMheZ4hAkmutedfqiU0Gg0KW/bpCf14Cil5/PgR55fbpLYglTUUDZr0KR59B5ftobs3CQtJ6/Kv4+IOohYSoghci8KNfAiqBIiEOE6CN/N8pwFCvf0nzpmJT2dN1SnJYZwlimOMNmMbXxfaLz4h6HW7BEGICiRRFPpmOtY3kw0DxWg0IlCKKIoIAoXJDVlaoI2m0WozylJUoACLKFKSOMQJSZFr+qMRQRiz1OkgsShZNml1JbwvK1PRoIKIwXBEmuUcHO6zur5Mr9vl05uf8ODhI46O9nj9jdf50pfeQuAQzvf51FiMsH7HL3t1SikwzvdtwVjvnxhLqBSFzhFCYa3EKc9VCwJhhA+niFmNUC0oaybs99NaE7zpU2nWajKllD5302hCIxge7fJHf/Db2P4ODWFJ6gHdYZd+v48uCjA+DNPvDTjMBLlWZEWBCkNqSUicJNSTGi1pSeoJViq6R13ykUYKTSMRNKRvSVhrLROGDVrNJUa6IJcBfSPRowF7j55w/94jjoYpKZIMixGS9e2zvP76W7z55pu8+MrnMTIpi89N2aYCP8/TQCFzsXXnx0KJ46luY1S0+ilKlwHo9rp0ez3qzQ6NZoOjoy5ZltFu+xBGLUkYDrs4DEY7ApWQ5Sn1hmKUjyi0B7k67Q6PHz+k3YzZ23vI1moDV2tjZIu4+5CVwV/Te/pdotYWYf8xVnYIrvyX9BsvIVohIRKVNpFBAWLSMQ9AKUnSWvnJASErJEJJjPOUf9ZYpBNoqzFY+sMhQRDS7/VZW92gKElRU50RNBKwBisMubHgLKNRShjW/E4UCJw2FFlGkXpkMggUYRQhMDQb3jRL0xw96JP1LcM0Z2l5FSkD31rAKRyWPM/LHUgikejClj6IxiBAKt/pq1nnB+/+gH/3x3/Eg88+Q8mAVqvJ1vY6uLcQUmElWOGtBelA2CoX1i8SjMFqXbLVgXAOox0Ch9EjHBKjLS5XSKVwiLEAVuGGKqQkq8oUW3Xznj0qbVGh4UIKdKFxwqGtZqQLjHC89IWvgM4Z9rrookdsMtpZQZ4bWs0lrBakaUEmBGmhieOIpFZHKIVUQWle58RxghOCs1KSj1JCoWlEkmar5YELFTPINLkWyFwTGI9YO2s598WAt5MGKggxzteRRqGk0+lw9swF4rhGlhYoFZUkcHqC0uLZBikF0z/rpIZzbPJPg4J+hfucZKpwQckoq/w6bMslklqdvPBdz8NQUau1iCKFsSOkTBBO0Wj4TuI6B6xCOEetFiC1BqvQekQcggoKtrbbxKFgpAUhGZj77B58RtK+RO3Mz2EP73Cw+yF69yYteZ4k3oLQQlB4q11MgEvvgp0sgqf7nDYgTVOs0ThrPMpmLEGoyPIhaV6gVECz1cQ4jXaawVEPazOEc4wGPfZ2nlJkQzqtFiqMQYRo4/j+O+8QRQnnLl5geXmFZr3O4cEB9+/fZ3t7izCMWFlb4+tf/0uuXLrMyuoa22fOIoOQmhCkWYHRBVL5jKQ0zShy7cu1At8MaZgaaipkOOrz2e1PyLKU/98f/C6j4RHWHdLvjjg8kty/dwmtcyQBhfGNcZWUhErhrB6j1sYWvrWC1pT4PEZ74ZXKV5N4+1vhgwVeEIWbaILKsrDGIKXy1SfCp/JNx4aVlKggICyfJ88ynDOYIkdbg7HGo7tWsrZ9CedgqSgwNkNJ6zlrnaPf6yGEoNls0kjqBCokiRMOuz1//SgGKYkbEcY6VBQjlPIkY8IRByEiz5HCl8SFYUieFj4pwcHQOeIoRklZtmv3VoIUEASSIIjI88K3TnQefDPWgvAW2ShNfZZM6Y+HYQilKzXvZ9p5/VKZtTifTloivt51suRF7tFjCXkxIgxBmwF5AVEcgBhh3RArUpAjjASnAiwxEg22TxQl9LtDlldaBGHg21LKHYTsY80ZjLzA+ou/hlMjuipANT9HffNlAtdBsoQolHfVwhxc6DtzM4vf/FTCeffTm4yGQ8JAoiQM+l2c0/R7PR4/fMTm5hZBHLN55iyFMdy5e48za2sEOmf36Q5plmKNod1u0T/ss3dwn1GhieKE5ZVV3nz7yzTbSzSabfKsD4EkrMV8/zvf5ebNm1y6eJGXXn6Fq9dfZHV9naIoyLKcIAyRZX5skRfEcYQI/YTcuvUJKhA0mi3ipEGhUz659SF//Vd/hsBy5eI2n37a40hYhsM+W5tn+eijD9FFjil8cbOUktwYdCAxOgMslJtTOkr9BBUFOi8o8gJnLfr/396Z7UhyHWf4O1tu1VXV1etMDzeRJmULsmwDviNMSgZs+BEM+MJPqAcwLMCwBBgQDFKWSAiWRXK4DYfk9FJrbmfzxcmq7hlStCzAMC8qLhrorkJ1IivjRMT//xERHUVZILVGSIVShihSX6RSAj0sD/LeDb2biVwheIRSwwiRgPOJYghC0nfJcbcTz1ODLhAdJnp03PbPJj1zlkkgG1qX0nc4GZWDY0miNCAUXknGRzMQCqk1IDBKksmIiIJoIWoFwhCDImg9OBY4L5GmJAiBwFOFHuFbtNAoY7A2pIMuKwlBEqJASI3JCpbLNaM8p+87lJI73tZkqSvnww8/5Oz09LaMeqZxIjIAZt7jh1o8hLDr6BkiCjGkbeQIicfjQ49zifrSWjA7mqK0woeO3t1QEJHC0nYdtkuHfZYFchMRdBwd5fS2pu9zmiagsxHBQV0/RumGdakIcYxgRhsjwR9TX0smk45JAcSKYNWQMTwtqvmmsvIbnfM3v/o59WbN9fUlJ8eH2L5BiEi93nB6dI52NQLLFx/9lnXT8OjxY64/e0i9XDAeT4jS8NIrr3L/+Zc4vXefZr0CF8jKCqkNeTXG5AXWRaK3jI9OODq7x/HpPV5/80dIBAfjMaPxIU2XFttmRUXfteTDZDmEoO16rG2p6zWr9ZyPPnrI4eyYvJzwyScfUGSRX7/7Nmenx1zcf8D86hojCspsxGbVUC8f86v/eAujcuqmp940FEXOydGUUZVTVQVSDKmtd6joUVKQ5xnkGSJCHyxCS5ACKfXgUGkSgvMOKdMelSIvEVIMg6o9xqT+TkgAUgKJtgj2LpcenHIg+6XGywJBTJPchyhutCIgiLvUKT1oQ/VLkIE4jDNFqPSaVEgh8UhUDIknTZeAGwh6LYb6OqlpEzspEolvtKHtOkxVEqWh7gKjyQlaGerVCheGGl0oOucoomc0GdO2Dda6IYoKrHdMZzNMnqLwarkcInU6PLb88KZuyIucvCx3KX/btBAC2hiapmF8OEXGgMlzhIjM51eMRgV1LTk5OeXy6gmTcUVRlJye5qxWVxwcjJlNcmLwbJorlFyhVUMkZWO9hbYTjCcXlMUB3npmU8eifsSq31BmE47K51hcz1F6zOj5DGks19eC6aggy5bEKBEDdfa70Off2zk368d8+ukjBIY6zzg9OWG5nNPWlqVccf/8gpv5nLrtWKwWnB0dcf/iOarJmNOz+5xePIftLFFqrI+MpycYrcjyAqJkvalp6yUxgrcdmTTgBIUZcXAySYOOQwTnESFCcNxcL6iqER5JFzxZWeG6mn/76c+olzc0Tc3k8JBMK956+21GueLjzz+GxuI2HR+9/yHXlwtW6w5pNNdffkHftfzzT/6JV154CesD89WK119/ncnhCCUkSqS1C1EEogaTF0gl8c7vUFc9oM+JSFdsy6C7XRlbimm7ai/GtEPzLiKut/QTkMl8QJ/9U2IL5zw+iAHFTYoWJVOElVGkaQaIXV/ttsYNIgxjU27pJyXlkB4mR06qGtLvIR0MdyQcRDxCBKRIa+xj1GS5ZL1eMzmaMT2eYENguVxQSJ0WF8SINBqdF4ymh9TrDbYPZFmBs5EYJdY6nA188fkTzs7PMVlF23UonZrUQ/B0rgOlMHnBk8tLYoyMRiNMlrNerVAxUo5GCCm5ubohxshsdsj08ARnHX0PJquYHs5YLK9wwXJwcERWRj5/csnBwThNMeyTqkcisb6nazW9lZycv0RwOZfXDyE0jA+OmI7GXN50SApkWTAan3J1fYOJmuPRCcWZZLVcoDDoO8jzFqX+uo1tv5dzvvzqa1zerLh8suSPv/cC3/2T75EVOcF5HDllNeLce+qmRSqByTOOD2dMqoKuaalXbQJZokVnGWVVECOs12uCj9R1u3sogmvpgyXPC/I8pTnOW7RRSKlp6gadZRjv0sPne7p6zaPPrvjgt//Je795F9fWZEazaZY8ePA854c5b/37zzk5nmFD4JPPv+T47B5PFhv6LlCODC++/Bp/9oPv8+d/+n3+6DsvM6oqMmMoqxJvHUamGURpto7AcJtq5aXaLfa1PtU8d6kSqdSAIg5xZ0sHCIHOcnYT1u+om+QgG4shpvR0ALT0oKwKIZBpgyBFViFFasIWW0Dl9kS+VWsNyO9A1m//57PTFJ8VlWg98MWor7yePjeNV3EDci3Z6iMEvRhUNpCiY4yMqgprLVmeJSxDCLwAaQzRp6VPUclEYWQaW69Z36yRUuKDZzoZowce2TvLdDplsVwMdzcQbUxZjlQYlaGVQUQNIpUSxJQSLBfrJPQXihjA2rTE2doOSKWI6zSrOqCyDKEVAY0ko7MOYwqCg/m8BgLWBtQoJwZNb3tGBxXr9RpCRESHwANZ6kp5ptb8g2vOyXTG3//DP2L0iCIfI7VJ3GdMDb9CpnXqLvi0RTlGnOupO0eMkvlqibOW+/fvk+cZwTnqpiHGyNXVNSCRQvH++x/w6qsvkmeKulmwWgesdVRVSZ4XFFmZTmAhMWWFkoDvePLZB7zz7i+4vrkCei5vnvDd115DSfj80QdcnJ0zqjI++vRTVn2g7QPrz644u/gOP/zhX/P+e+/xd3/7N5yeHnPv/JyyyDFSIGNKl43OUyopZZozG1JM2kWxHfcm2IJuQplhUvhQ9Os7/NzwGVtnF+J2gNWWA43Ibda544WV0gNdJZIkUaR9DUpt+dI7Kqk4TKIQdwQN2xEoMiaw5tnJEHcEI8/2VgohkeKr4Mz22uywVLgoS5y13Hz5JYezGWVWsLicU41GhBjo247FzZyDgwPysqAoctabDd57+r6n3myYjMcg05a29WaJMgL6wOz4iM16jZIC17UYKTk5ntG1LbPpBEGkaTcUeUHfNigUhSnwHnARH11yOuvxfUA4gSlyCApvkzSyzCtC8PRNDx6czTDZPY5ODgnB0vWWvnHkJkNxBEaQTUuca6jrOZDhLHjrh+cz4vo+lSYhyRWFVF+5j99k38hzfvLOT2KIhq4XFNUho8mMrCjxvsP3Pd4lVY0yqcbyMdB7x3wx5+HDh1xfXvLKK6/x/MVzVGVF3awpqhIpJV3X07Y9WZZRVgcQHMT0RWut01QAk2bu2N7S2ciq7VltNgTbcPPFx3SrK9555xe4GOl8ZLnacDidELqGerPi4vyCx0/mfPrlDfNlgwuRN994gxACs6ND3nz9rzidzTDD8qUgkshfpyFIDBqGBIYIEDFRNVuh/uDDKWKJ2/0wd9PGWzrk69UvW4sxDoBXBySVVZ7nT71vJ4EU6ik54lOKLYb0OiYlShgoBiHFIDa/Haz2u+zpZ0IgeLqb6K4e2vqkrNmJuLOcru+SsNwnhVcApE5psFAK23VIBNalelQPaX6WZ1jXoqSkLAqElAlU3Ebegyqh5sMCrW1pcDAeo7WBCN4H6rol+KHSljCdjgDJ/GaJ90mXfHw0ZVM3OB/RWlAUGXVT412kbS1FmZxVKU2RZ4xGFdY6mqbGWoeSJVob8jIiRM9qvaHZQJFrDmcTFvP57lkuy5KiKHarMO/eY6UUphp/7Zfxjc75y3/9cXzwwstImaN1RVSGGIcHIYILaSCyiLDebGi94/TeCUp7FvMFrrXcO7tP1/QURaJbhIxkWZ6mxwG9TXNbqyGN3F5w33dImZQx7777S5rOQV7x6LPHbJZX+Pqa00nBz376L0Sp0MWI1bpO83uCY71Y8YO/+EuizPn1fz1kdnzOj954gxcfPOC55y9QRiJjpFAKYkAowQ4JCUMnAxGdJdQ1xoHn3eL1Q8uIFGIANbapZErT/ZbTHBTuSSO8jaDseM0YbveTxpgOB+99qrW2MshB9ytlQmfl0NuZPpOBBx0+UKrkvDHsUtmtsi2J4m8j//8kids58DMqprs0gL/zNyXkblUeEbyNu01nISZUWg7v2cFUA+8rxLaBIu1s0TJROSF6tNJpXw+pvWdbE6f+VoFUmuB3cO1TByMDrRMRxJCENErJdBjHSAgkMXpwbDZrNnXD4fSIGAPKpHuZUtF0jalDK4lNpFQgOkJsBx1AWgjFMOaU4cB6ajP3jv5JP6VUmPLgf++ce9vb3v7/7A9b87W3ve3t/9z2zrm3vX1Lbe+ce9vbt9T2zrm3vX1Lbe+ce9vbt9T2zrm3vX1L7b8BMnEVJPZb0QQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测:  一个 戴着 墨镜 的 女人 走 在 道路 上 的 汽车 旁\n",
      "真实： 一个 右肩 挎着 包 的 女人 走 在 干净 的 道路 上\n"
     ]
    }
   ],
   "source": [
    "generate_caption()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测:  车厢 里 一个 穿着 西装 的 男人 和 一个 穿着 婚纱 的 女人 坐在 椅子 上\n",
      "真实： 明亮 的 车厢 里 坐 着 一对 拿 着 手机 的 男女\n"
     ]
    }
   ],
   "source": [
    "generate_caption()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测:  大厅 里 一个 人 的 旁边 走 着 一个 右手 拿 着 手机 的 女人 走 在 人群 前\n",
      "真实： 街道 上 的 人群 旁 走 着 一个 右肩 挎包 、 左手 拿 东西 的 女人\n"
     ]
    }
   ],
   "source": [
    "generate_caption()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存权重\n",
    "# caption_model.save_weights(\"./my_model/checkpoint\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3.10.6 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
